// // This file is auto-generated. Please don't modify it! // #pragma once #ifdef __cplusplus //#import "opencv.hpp" #import "opencv2/imgproc.hpp" #import "imgproc/bindings.hpp" #else #define CV_EXPORTS #endif #import #import "Core.h" @class CLAHE; @class FloatVector; @class GeneralizedHoughBallard; @class GeneralizedHoughGuil; @class Int4; @class IntVector; @class LineSegmentDetector; @class Mat; @class Moments; @class Point2d; @class Point2f; @class Point2i; @class Rect2i; @class RotatedRect; @class Scalar; @class Size2i; @class TermCriteria; // C++: enum AdaptiveThresholdTypes (cv.AdaptiveThresholdTypes) typedef NS_ENUM(int, AdaptiveThresholdTypes) { ADAPTIVE_THRESH_MEAN_C = 0, ADAPTIVE_THRESH_GAUSSIAN_C = 1 }; // C++: enum ColorConversionCodes (cv.ColorConversionCodes) typedef NS_ENUM(int, ColorConversionCodes) { COLOR_BGR2BGRA = 0, COLOR_RGB2RGBA = COLOR_BGR2BGRA, COLOR_BGRA2BGR = 1, COLOR_RGBA2RGB = COLOR_BGRA2BGR, COLOR_BGR2RGBA = 2, COLOR_RGB2BGRA = COLOR_BGR2RGBA, COLOR_RGBA2BGR = 3, COLOR_BGRA2RGB = COLOR_RGBA2BGR, COLOR_BGR2RGB = 4, COLOR_RGB2BGR = COLOR_BGR2RGB, COLOR_BGRA2RGBA = 5, COLOR_RGBA2BGRA = COLOR_BGRA2RGBA, COLOR_BGR2GRAY = 6, COLOR_RGB2GRAY = 7, COLOR_GRAY2BGR = 8, COLOR_GRAY2RGB = COLOR_GRAY2BGR, COLOR_GRAY2BGRA = 9, COLOR_GRAY2RGBA = COLOR_GRAY2BGRA, COLOR_BGRA2GRAY = 10, COLOR_RGBA2GRAY = 11, COLOR_BGR2BGR565 = 12, COLOR_RGB2BGR565 = 13, COLOR_BGR5652BGR = 14, COLOR_BGR5652RGB = 15, COLOR_BGRA2BGR565 = 16, COLOR_RGBA2BGR565 = 17, COLOR_BGR5652BGRA = 18, COLOR_BGR5652RGBA = 19, COLOR_GRAY2BGR565 = 20, COLOR_BGR5652GRAY = 21, COLOR_BGR2BGR555 = 22, COLOR_RGB2BGR555 = 23, COLOR_BGR5552BGR = 24, COLOR_BGR5552RGB = 25, COLOR_BGRA2BGR555 = 26, COLOR_RGBA2BGR555 = 27, COLOR_BGR5552BGRA = 28, COLOR_BGR5552RGBA = 29, COLOR_GRAY2BGR555 = 30, COLOR_BGR5552GRAY = 31, COLOR_BGR2XYZ = 32, COLOR_RGB2XYZ = 33, COLOR_XYZ2BGR = 34, COLOR_XYZ2RGB = 35, COLOR_BGR2YCrCb = 36, COLOR_RGB2YCrCb = 37, COLOR_YCrCb2BGR = 38, COLOR_YCrCb2RGB = 39, COLOR_BGR2HSV = 40, COLOR_RGB2HSV = 41, COLOR_BGR2Lab = 44, COLOR_RGB2Lab = 45, COLOR_BGR2Luv = 50, COLOR_RGB2Luv = 51, COLOR_BGR2HLS = 52, COLOR_RGB2HLS = 53, COLOR_HSV2BGR = 54, COLOR_HSV2RGB = 55, COLOR_Lab2BGR = 56, COLOR_Lab2RGB = 57, COLOR_Luv2BGR = 58, COLOR_Luv2RGB = 59, COLOR_HLS2BGR = 60, COLOR_HLS2RGB = 61, COLOR_BGR2HSV_FULL = 66, COLOR_RGB2HSV_FULL = 67, COLOR_BGR2HLS_FULL = 68, COLOR_RGB2HLS_FULL = 69, COLOR_HSV2BGR_FULL = 70, COLOR_HSV2RGB_FULL = 71, COLOR_HLS2BGR_FULL = 72, COLOR_HLS2RGB_FULL = 73, COLOR_LBGR2Lab = 74, COLOR_LRGB2Lab = 75, COLOR_LBGR2Luv = 76, COLOR_LRGB2Luv = 77, COLOR_Lab2LBGR = 78, COLOR_Lab2LRGB = 79, COLOR_Luv2LBGR = 80, COLOR_Luv2LRGB = 81, COLOR_BGR2YUV = 82, COLOR_RGB2YUV = 83, COLOR_YUV2BGR = 84, COLOR_YUV2RGB = 85, COLOR_YUV2RGB_NV12 = 90, COLOR_YUV2BGR_NV12 = 91, COLOR_YUV2RGB_NV21 = 92, COLOR_YUV2BGR_NV21 = 93, COLOR_YUV420sp2RGB = COLOR_YUV2RGB_NV21, COLOR_YUV420sp2BGR = COLOR_YUV2BGR_NV21, COLOR_YUV2RGBA_NV12 = 94, COLOR_YUV2BGRA_NV12 = 95, COLOR_YUV2RGBA_NV21 = 96, COLOR_YUV2BGRA_NV21 = 97, COLOR_YUV420sp2RGBA = COLOR_YUV2RGBA_NV21, COLOR_YUV420sp2BGRA = COLOR_YUV2BGRA_NV21, COLOR_YUV2RGB_YV12 = 98, COLOR_YUV2BGR_YV12 = 99, COLOR_YUV2RGB_IYUV = 100, COLOR_YUV2BGR_IYUV = 101, COLOR_YUV2RGB_I420 = COLOR_YUV2RGB_IYUV, COLOR_YUV2BGR_I420 = COLOR_YUV2BGR_IYUV, COLOR_YUV420p2RGB = COLOR_YUV2RGB_YV12, COLOR_YUV420p2BGR = COLOR_YUV2BGR_YV12, COLOR_YUV2RGBA_YV12 = 102, COLOR_YUV2BGRA_YV12 = 103, COLOR_YUV2RGBA_IYUV = 104, COLOR_YUV2BGRA_IYUV = 105, COLOR_YUV2RGBA_I420 = COLOR_YUV2RGBA_IYUV, COLOR_YUV2BGRA_I420 = COLOR_YUV2BGRA_IYUV, COLOR_YUV420p2RGBA = COLOR_YUV2RGBA_YV12, COLOR_YUV420p2BGRA = COLOR_YUV2BGRA_YV12, COLOR_YUV2GRAY_420 = 106, COLOR_YUV2GRAY_NV21 = COLOR_YUV2GRAY_420, COLOR_YUV2GRAY_NV12 = COLOR_YUV2GRAY_420, COLOR_YUV2GRAY_YV12 = COLOR_YUV2GRAY_420, COLOR_YUV2GRAY_IYUV = COLOR_YUV2GRAY_420, COLOR_YUV2GRAY_I420 = COLOR_YUV2GRAY_420, COLOR_YUV420sp2GRAY = COLOR_YUV2GRAY_420, COLOR_YUV420p2GRAY = COLOR_YUV2GRAY_420, COLOR_YUV2RGB_UYVY = 107, COLOR_YUV2BGR_UYVY = 108, COLOR_YUV2RGB_Y422 = COLOR_YUV2RGB_UYVY, COLOR_YUV2BGR_Y422 = COLOR_YUV2BGR_UYVY, COLOR_YUV2RGB_UYNV = COLOR_YUV2RGB_UYVY, COLOR_YUV2BGR_UYNV = COLOR_YUV2BGR_UYVY, COLOR_YUV2RGBA_UYVY = 111, COLOR_YUV2BGRA_UYVY = 112, COLOR_YUV2RGBA_Y422 = COLOR_YUV2RGBA_UYVY, COLOR_YUV2BGRA_Y422 = COLOR_YUV2BGRA_UYVY, COLOR_YUV2RGBA_UYNV = COLOR_YUV2RGBA_UYVY, COLOR_YUV2BGRA_UYNV = COLOR_YUV2BGRA_UYVY, COLOR_YUV2RGB_YUY2 = 115, COLOR_YUV2BGR_YUY2 = 116, COLOR_YUV2RGB_YVYU = 117, COLOR_YUV2BGR_YVYU = 118, COLOR_YUV2RGB_YUYV = COLOR_YUV2RGB_YUY2, COLOR_YUV2BGR_YUYV = COLOR_YUV2BGR_YUY2, COLOR_YUV2RGB_YUNV = COLOR_YUV2RGB_YUY2, COLOR_YUV2BGR_YUNV = COLOR_YUV2BGR_YUY2, COLOR_YUV2RGBA_YUY2 = 119, COLOR_YUV2BGRA_YUY2 = 120, COLOR_YUV2RGBA_YVYU = 121, COLOR_YUV2BGRA_YVYU = 122, COLOR_YUV2RGBA_YUYV = COLOR_YUV2RGBA_YUY2, COLOR_YUV2BGRA_YUYV = COLOR_YUV2BGRA_YUY2, COLOR_YUV2RGBA_YUNV = COLOR_YUV2RGBA_YUY2, COLOR_YUV2BGRA_YUNV = COLOR_YUV2BGRA_YUY2, COLOR_YUV2GRAY_UYVY = 123, COLOR_YUV2GRAY_YUY2 = 124, COLOR_YUV2GRAY_Y422 = COLOR_YUV2GRAY_UYVY, COLOR_YUV2GRAY_UYNV = COLOR_YUV2GRAY_UYVY, COLOR_YUV2GRAY_YVYU = COLOR_YUV2GRAY_YUY2, COLOR_YUV2GRAY_YUYV = COLOR_YUV2GRAY_YUY2, COLOR_YUV2GRAY_YUNV = COLOR_YUV2GRAY_YUY2, COLOR_RGBA2mRGBA = 125, COLOR_mRGBA2RGBA = 126, COLOR_RGB2YUV_I420 = 127, COLOR_BGR2YUV_I420 = 128, COLOR_RGB2YUV_IYUV = COLOR_RGB2YUV_I420, COLOR_BGR2YUV_IYUV = COLOR_BGR2YUV_I420, COLOR_RGBA2YUV_I420 = 129, COLOR_BGRA2YUV_I420 = 130, COLOR_RGBA2YUV_IYUV = COLOR_RGBA2YUV_I420, COLOR_BGRA2YUV_IYUV = COLOR_BGRA2YUV_I420, COLOR_RGB2YUV_YV12 = 131, COLOR_BGR2YUV_YV12 = 132, COLOR_RGBA2YUV_YV12 = 133, COLOR_BGRA2YUV_YV12 = 134, COLOR_BayerBG2BGR = 46, COLOR_BayerGB2BGR = 47, COLOR_BayerRG2BGR = 48, COLOR_BayerGR2BGR = 49, COLOR_BayerRGGB2BGR = COLOR_BayerBG2BGR, COLOR_BayerGRBG2BGR = COLOR_BayerGB2BGR, COLOR_BayerBGGR2BGR = COLOR_BayerRG2BGR, COLOR_BayerGBRG2BGR = COLOR_BayerGR2BGR, COLOR_BayerRGGB2RGB = COLOR_BayerBGGR2BGR, COLOR_BayerGRBG2RGB = COLOR_BayerGBRG2BGR, COLOR_BayerBGGR2RGB = COLOR_BayerRGGB2BGR, COLOR_BayerGBRG2RGB = COLOR_BayerGRBG2BGR, COLOR_BayerBG2RGB = COLOR_BayerRG2BGR, COLOR_BayerGB2RGB = COLOR_BayerGR2BGR, COLOR_BayerRG2RGB = COLOR_BayerBG2BGR, COLOR_BayerGR2RGB = COLOR_BayerGB2BGR, COLOR_BayerBG2GRAY = 86, COLOR_BayerGB2GRAY = 87, COLOR_BayerRG2GRAY = 88, COLOR_BayerGR2GRAY = 89, COLOR_BayerRGGB2GRAY = COLOR_BayerBG2GRAY, COLOR_BayerGRBG2GRAY = COLOR_BayerGB2GRAY, COLOR_BayerBGGR2GRAY = COLOR_BayerRG2GRAY, COLOR_BayerGBRG2GRAY = COLOR_BayerGR2GRAY, COLOR_BayerBG2BGR_VNG = 62, COLOR_BayerGB2BGR_VNG = 63, COLOR_BayerRG2BGR_VNG = 64, COLOR_BayerGR2BGR_VNG = 65, COLOR_BayerRGGB2BGR_VNG = COLOR_BayerBG2BGR_VNG, COLOR_BayerGRBG2BGR_VNG = COLOR_BayerGB2BGR_VNG, COLOR_BayerBGGR2BGR_VNG = COLOR_BayerRG2BGR_VNG, COLOR_BayerGBRG2BGR_VNG = COLOR_BayerGR2BGR_VNG, COLOR_BayerRGGB2RGB_VNG = COLOR_BayerBGGR2BGR_VNG, COLOR_BayerGRBG2RGB_VNG = COLOR_BayerGBRG2BGR_VNG, COLOR_BayerBGGR2RGB_VNG = COLOR_BayerRGGB2BGR_VNG, COLOR_BayerGBRG2RGB_VNG = COLOR_BayerGRBG2BGR_VNG, COLOR_BayerBG2RGB_VNG = COLOR_BayerRG2BGR_VNG, COLOR_BayerGB2RGB_VNG = COLOR_BayerGR2BGR_VNG, COLOR_BayerRG2RGB_VNG = COLOR_BayerBG2BGR_VNG, COLOR_BayerGR2RGB_VNG = COLOR_BayerGB2BGR_VNG, COLOR_BayerBG2BGR_EA = 135, COLOR_BayerGB2BGR_EA = 136, COLOR_BayerRG2BGR_EA = 137, COLOR_BayerGR2BGR_EA = 138, COLOR_BayerRGGB2BGR_EA = COLOR_BayerBG2BGR_EA, COLOR_BayerGRBG2BGR_EA = COLOR_BayerGB2BGR_EA, COLOR_BayerBGGR2BGR_EA = COLOR_BayerRG2BGR_EA, COLOR_BayerGBRG2BGR_EA = COLOR_BayerGR2BGR_EA, COLOR_BayerRGGB2RGB_EA = COLOR_BayerBGGR2BGR_EA, COLOR_BayerGRBG2RGB_EA = COLOR_BayerGBRG2BGR_EA, COLOR_BayerBGGR2RGB_EA = COLOR_BayerRGGB2BGR_EA, COLOR_BayerGBRG2RGB_EA = COLOR_BayerGRBG2BGR_EA, COLOR_BayerBG2RGB_EA = COLOR_BayerRG2BGR_EA, COLOR_BayerGB2RGB_EA = COLOR_BayerGR2BGR_EA, COLOR_BayerRG2RGB_EA = COLOR_BayerBG2BGR_EA, COLOR_BayerGR2RGB_EA = COLOR_BayerGB2BGR_EA, COLOR_BayerBG2BGRA = 139, COLOR_BayerGB2BGRA = 140, COLOR_BayerRG2BGRA = 141, COLOR_BayerGR2BGRA = 142, COLOR_BayerRGGB2BGRA = COLOR_BayerBG2BGRA, COLOR_BayerGRBG2BGRA = COLOR_BayerGB2BGRA, COLOR_BayerBGGR2BGRA = COLOR_BayerRG2BGRA, COLOR_BayerGBRG2BGRA = COLOR_BayerGR2BGRA, COLOR_BayerRGGB2RGBA = COLOR_BayerBGGR2BGRA, COLOR_BayerGRBG2RGBA = COLOR_BayerGBRG2BGRA, COLOR_BayerBGGR2RGBA = COLOR_BayerRGGB2BGRA, COLOR_BayerGBRG2RGBA = COLOR_BayerGRBG2BGRA, COLOR_BayerBG2RGBA = COLOR_BayerRG2BGRA, COLOR_BayerGB2RGBA = COLOR_BayerGR2BGRA, COLOR_BayerRG2RGBA = COLOR_BayerBG2BGRA, COLOR_BayerGR2RGBA = COLOR_BayerGB2BGRA, COLOR_COLORCVT_MAX = 143 }; // C++: enum ColormapTypes (cv.ColormapTypes) typedef NS_ENUM(int, ColormapTypes) { COLORMAP_AUTUMN = 0, COLORMAP_BONE = 1, COLORMAP_JET = 2, COLORMAP_WINTER = 3, COLORMAP_RAINBOW = 4, COLORMAP_OCEAN = 5, COLORMAP_SUMMER = 6, COLORMAP_SPRING = 7, COLORMAP_COOL = 8, COLORMAP_HSV = 9, COLORMAP_PINK = 10, COLORMAP_HOT = 11, COLORMAP_PARULA = 12, COLORMAP_MAGMA = 13, COLORMAP_INFERNO = 14, COLORMAP_PLASMA = 15, COLORMAP_VIRIDIS = 16, COLORMAP_CIVIDIS = 17, COLORMAP_TWILIGHT = 18, COLORMAP_TWILIGHT_SHIFTED = 19, COLORMAP_TURBO = 20, COLORMAP_DEEPGREEN = 21 }; // C++: enum ConnectedComponentsAlgorithmsTypes (cv.ConnectedComponentsAlgorithmsTypes) typedef NS_ENUM(int, ConnectedComponentsAlgorithmsTypes) { CCL_DEFAULT = -1, CCL_WU = 0, CCL_GRANA = 1, CCL_BOLELLI = 2, CCL_SAUF = 3, CCL_BBDT = 4, CCL_SPAGHETTI = 5 }; // C++: enum ConnectedComponentsTypes (cv.ConnectedComponentsTypes) typedef NS_ENUM(int, ConnectedComponentsTypes) { CC_STAT_LEFT = 0, CC_STAT_TOP = 1, CC_STAT_WIDTH = 2, CC_STAT_HEIGHT = 3, CC_STAT_AREA = 4, CC_STAT_MAX = 5 }; // C++: enum ContourApproximationModes (cv.ContourApproximationModes) typedef NS_ENUM(int, ContourApproximationModes) { CHAIN_APPROX_NONE = 1, CHAIN_APPROX_SIMPLE = 2, CHAIN_APPROX_TC89_L1 = 3, CHAIN_APPROX_TC89_KCOS = 4 }; // C++: enum DistanceTransformLabelTypes (cv.DistanceTransformLabelTypes) typedef NS_ENUM(int, DistanceTransformLabelTypes) { DIST_LABEL_CCOMP = 0, DIST_LABEL_PIXEL = 1 }; // C++: enum DistanceTransformMasks (cv.DistanceTransformMasks) typedef NS_ENUM(int, DistanceTransformMasks) { DIST_MASK_3 = 3, DIST_MASK_5 = 5, DIST_MASK_PRECISE = 0 }; // C++: enum DistanceTypes (cv.DistanceTypes) typedef NS_ENUM(int, DistanceTypes) { DIST_USER = -1, DIST_L1 = 1, DIST_L2 = 2, DIST_C = 3, DIST_L12 = 4, DIST_FAIR = 5, DIST_WELSCH = 6, DIST_HUBER = 7 }; // C++: enum FloodFillFlags (cv.FloodFillFlags) typedef NS_ENUM(int, FloodFillFlags) { FLOODFILL_FIXED_RANGE = 1 << 16, FLOODFILL_MASK_ONLY = 1 << 17 }; // C++: enum GrabCutClasses (cv.GrabCutClasses) typedef NS_ENUM(int, GrabCutClasses) { GC_BGD = 0, GC_FGD = 1, GC_PR_BGD = 2, GC_PR_FGD = 3 }; // C++: enum GrabCutModes (cv.GrabCutModes) typedef NS_ENUM(int, GrabCutModes) { GC_INIT_WITH_RECT = 0, GC_INIT_WITH_MASK = 1, GC_EVAL = 2, GC_EVAL_FREEZE_MODEL = 3 }; // C++: enum HersheyFonts (cv.HersheyFonts) typedef NS_ENUM(int, HersheyFonts) { FONT_HERSHEY_SIMPLEX = 0, FONT_HERSHEY_PLAIN = 1, FONT_HERSHEY_DUPLEX = 2, FONT_HERSHEY_COMPLEX = 3, FONT_HERSHEY_TRIPLEX = 4, FONT_HERSHEY_COMPLEX_SMALL = 5, FONT_HERSHEY_SCRIPT_SIMPLEX = 6, FONT_HERSHEY_SCRIPT_COMPLEX = 7, FONT_ITALIC = 16 }; // C++: enum HistCompMethods (cv.HistCompMethods) typedef NS_ENUM(int, HistCompMethods) { HISTCMP_CORREL = 0, HISTCMP_CHISQR = 1, HISTCMP_INTERSECT = 2, HISTCMP_BHATTACHARYYA = 3, HISTCMP_HELLINGER = HISTCMP_BHATTACHARYYA, HISTCMP_CHISQR_ALT = 4, HISTCMP_KL_DIV = 5 }; // C++: enum HoughModes (cv.HoughModes) typedef NS_ENUM(int, HoughModes) { HOUGH_STANDARD = 0, HOUGH_PROBABILISTIC = 1, HOUGH_MULTI_SCALE = 2, HOUGH_GRADIENT = 3, HOUGH_GRADIENT_ALT = 4 }; // C++: enum InterpolationFlags (cv.InterpolationFlags) typedef NS_ENUM(int, InterpolationFlags) { INTER_NEAREST = 0, INTER_LINEAR = 1, INTER_CUBIC = 2, INTER_AREA = 3, INTER_LANCZOS4 = 4, INTER_LINEAR_EXACT = 5, INTER_NEAREST_EXACT = 6, INTER_MAX = 7, WARP_FILL_OUTLIERS = 8, WARP_INVERSE_MAP = 16 }; // C++: enum InterpolationMasks (cv.InterpolationMasks) typedef NS_ENUM(int, InterpolationMasks) { INTER_BITS = 5, INTER_BITS2 = INTER_BITS * 2, INTER_TAB_SIZE = 1 << INTER_BITS, INTER_TAB_SIZE2 = INTER_TAB_SIZE * INTER_TAB_SIZE }; // C++: enum LineSegmentDetectorModes (cv.LineSegmentDetectorModes) typedef NS_ENUM(int, LineSegmentDetectorModes) { LSD_REFINE_NONE = 0, LSD_REFINE_STD = 1, LSD_REFINE_ADV = 2 }; // C++: enum LineTypes (cv.LineTypes) typedef NS_ENUM(int, LineTypes) { FILLED = -1, LINE_4 = 4, LINE_8 = 8, LINE_AA = 16 }; // C++: enum MarkerTypes (cv.MarkerTypes) typedef NS_ENUM(int, MarkerTypes) { MARKER_CROSS = 0, MARKER_TILTED_CROSS = 1, MARKER_STAR = 2, MARKER_DIAMOND = 3, MARKER_SQUARE = 4, MARKER_TRIANGLE_UP = 5, MARKER_TRIANGLE_DOWN = 6 }; // C++: enum MorphShapes (cv.MorphShapes) typedef NS_ENUM(int, MorphShapes) { MORPH_RECT = 0, MORPH_CROSS = 1, MORPH_ELLIPSE = 2 }; // C++: enum MorphTypes (cv.MorphTypes) typedef NS_ENUM(int, MorphTypes) { MORPH_ERODE = 0, MORPH_DILATE = 1, MORPH_OPEN = 2, MORPH_CLOSE = 3, MORPH_GRADIENT = 4, MORPH_TOPHAT = 5, MORPH_BLACKHAT = 6, MORPH_HITMISS = 7 }; // C++: enum RectanglesIntersectTypes (cv.RectanglesIntersectTypes) typedef NS_ENUM(int, RectanglesIntersectTypes) { INTERSECT_NONE = 0, INTERSECT_PARTIAL = 1, INTERSECT_FULL = 2 }; // C++: enum RetrievalModes (cv.RetrievalModes) typedef NS_ENUM(int, RetrievalModes) { RETR_EXTERNAL = 0, RETR_LIST = 1, RETR_CCOMP = 2, RETR_TREE = 3, RETR_FLOODFILL = 4 }; // C++: enum ShapeMatchModes (cv.ShapeMatchModes) typedef NS_ENUM(int, ShapeMatchModes) { CONTOURS_MATCH_I1 = 1, CONTOURS_MATCH_I2 = 2, CONTOURS_MATCH_I3 = 3 }; // C++: enum SpecialFilter (cv.SpecialFilter) typedef NS_ENUM(int, SpecialFilter) { FILTER_SCHARR = -1 }; // C++: enum TemplateMatchModes (cv.TemplateMatchModes) typedef NS_ENUM(int, TemplateMatchModes) { TM_SQDIFF = 0, TM_SQDIFF_NORMED = 1, TM_CCORR = 2, TM_CCORR_NORMED = 3, TM_CCOEFF = 4, TM_CCOEFF_NORMED = 5 }; // C++: enum ThresholdTypes (cv.ThresholdTypes) typedef NS_ENUM(int, ThresholdTypes) { THRESH_BINARY = 0, THRESH_BINARY_INV = 1, THRESH_TRUNC = 2, THRESH_TOZERO = 3, THRESH_TOZERO_INV = 4, THRESH_MASK = 7, THRESH_OTSU = 8, THRESH_TRIANGLE = 16 }; // C++: enum WarpPolarMode (cv.WarpPolarMode) typedef NS_ENUM(int, WarpPolarMode) { WARP_POLAR_LINEAR = 0, WARP_POLAR_LOG = 256 }; NS_ASSUME_NONNULL_BEGIN // C++: class Imgproc /** * The Imgproc module * * Member classes: `GeneralizedHough`, `GeneralizedHoughBallard`, `GeneralizedHoughGuil`, `CLAHE`, `Subdiv2D`, `LineSegmentDetector`, `IntelligentScissorsMB`, `Moments` * * Member enums: `SpecialFilter`, `MorphTypes`, `MorphShapes`, `InterpolationFlags`, `WarpPolarMode`, `InterpolationMasks`, `DistanceTypes`, `DistanceTransformMasks`, `ThresholdTypes`, `AdaptiveThresholdTypes`, `GrabCutClasses`, `GrabCutModes`, `DistanceTransformLabelTypes`, `FloodFillFlags`, `ConnectedComponentsTypes`, `ConnectedComponentsAlgorithmsTypes`, `RetrievalModes`, `ContourApproximationModes`, `ShapeMatchModes`, `HoughModes`, `LineSegmentDetectorModes`, `HistCompMethods`, `ColorConversionCodes`, `RectanglesIntersectTypes`, `LineTypes`, `HersheyFonts`, `MarkerTypes`, `TemplateMatchModes`, `ColormapTypes` */ CV_EXPORTS @interface Imgproc : NSObject #pragma mark - Class Constants @property (class, readonly) int CV_GAUSSIAN_5x5 NS_SWIFT_NAME(CV_GAUSSIAN_5x5); @property (class, readonly) int CV_SCHARR NS_SWIFT_NAME(CV_SCHARR); @property (class, readonly) int CV_MAX_SOBEL_KSIZE NS_SWIFT_NAME(CV_MAX_SOBEL_KSIZE); @property (class, readonly) int CV_RGBA2mRGBA NS_SWIFT_NAME(CV_RGBA2mRGBA); @property (class, readonly) int CV_mRGBA2RGBA NS_SWIFT_NAME(CV_mRGBA2RGBA); @property (class, readonly) int CV_WARP_FILL_OUTLIERS NS_SWIFT_NAME(CV_WARP_FILL_OUTLIERS); @property (class, readonly) int CV_WARP_INVERSE_MAP NS_SWIFT_NAME(CV_WARP_INVERSE_MAP); @property (class, readonly) int CV_CHAIN_CODE NS_SWIFT_NAME(CV_CHAIN_CODE); @property (class, readonly) int CV_LINK_RUNS NS_SWIFT_NAME(CV_LINK_RUNS); @property (class, readonly) int CV_POLY_APPROX_DP NS_SWIFT_NAME(CV_POLY_APPROX_DP); @property (class, readonly) int CV_CLOCKWISE NS_SWIFT_NAME(CV_CLOCKWISE); @property (class, readonly) int CV_COUNTER_CLOCKWISE NS_SWIFT_NAME(CV_COUNTER_CLOCKWISE); @property (class, readonly) int CV_CANNY_L2_GRADIENT NS_SWIFT_NAME(CV_CANNY_L2_GRADIENT); #pragma mark - Methods // // Ptr_LineSegmentDetector cv::createLineSegmentDetector(int refine = LSD_REFINE_STD, double scale = 0.8, double sigma_scale = 0.6, double quant = 2.0, double ang_th = 22.5, double log_eps = 0, double density_th = 0.7, int n_bins = 1024) // /** * Creates a smart pointer to a LineSegmentDetector object and initializes it. * * The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want * to edit those, as to tailor it for their own application. * * @param refine The way found lines will be refined, see #LineSegmentDetectorModes * @param scale The scale of the image that will be used to find the lines. Range (0..1]. * @param sigma_scale Sigma for Gaussian filter. It is computed as sigma = sigma_scale/scale. * @param quant Bound to the quantization error on the gradient norm. * @param ang_th Gradient angle tolerance in degrees. * @param log_eps Detection threshold: -log10(NFA) \> log_eps. Used only when advance refinement is chosen. * @param density_th Minimal density of aligned region points in the enclosing rectangle. * @param n_bins Number of bins in pseudo-ordering of gradient modulus. */ + (LineSegmentDetector*)createLineSegmentDetector:(int)refine scale:(double)scale sigma_scale:(double)sigma_scale quant:(double)quant ang_th:(double)ang_th log_eps:(double)log_eps density_th:(double)density_th n_bins:(int)n_bins NS_SWIFT_NAME(createLineSegmentDetector(refine:scale:sigma_scale:quant:ang_th:log_eps:density_th:n_bins:)); /** * Creates a smart pointer to a LineSegmentDetector object and initializes it. * * The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want * to edit those, as to tailor it for their own application. * * @param refine The way found lines will be refined, see #LineSegmentDetectorModes * @param scale The scale of the image that will be used to find the lines. Range (0..1]. * @param sigma_scale Sigma for Gaussian filter. It is computed as sigma = sigma_scale/scale. * @param quant Bound to the quantization error on the gradient norm. * @param ang_th Gradient angle tolerance in degrees. * @param log_eps Detection threshold: -log10(NFA) \> log_eps. Used only when advance refinement is chosen. * @param density_th Minimal density of aligned region points in the enclosing rectangle. */ + (LineSegmentDetector*)createLineSegmentDetector:(int)refine scale:(double)scale sigma_scale:(double)sigma_scale quant:(double)quant ang_th:(double)ang_th log_eps:(double)log_eps density_th:(double)density_th NS_SWIFT_NAME(createLineSegmentDetector(refine:scale:sigma_scale:quant:ang_th:log_eps:density_th:)); /** * Creates a smart pointer to a LineSegmentDetector object and initializes it. * * The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want * to edit those, as to tailor it for their own application. * * @param refine The way found lines will be refined, see #LineSegmentDetectorModes * @param scale The scale of the image that will be used to find the lines. Range (0..1]. * @param sigma_scale Sigma for Gaussian filter. It is computed as sigma = sigma_scale/scale. * @param quant Bound to the quantization error on the gradient norm. * @param ang_th Gradient angle tolerance in degrees. * @param log_eps Detection threshold: -log10(NFA) \> log_eps. Used only when advance refinement is chosen. */ + (LineSegmentDetector*)createLineSegmentDetector:(int)refine scale:(double)scale sigma_scale:(double)sigma_scale quant:(double)quant ang_th:(double)ang_th log_eps:(double)log_eps NS_SWIFT_NAME(createLineSegmentDetector(refine:scale:sigma_scale:quant:ang_th:log_eps:)); /** * Creates a smart pointer to a LineSegmentDetector object and initializes it. * * The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want * to edit those, as to tailor it for their own application. * * @param refine The way found lines will be refined, see #LineSegmentDetectorModes * @param scale The scale of the image that will be used to find the lines. Range (0..1]. * @param sigma_scale Sigma for Gaussian filter. It is computed as sigma = sigma_scale/scale. * @param quant Bound to the quantization error on the gradient norm. * @param ang_th Gradient angle tolerance in degrees. */ + (LineSegmentDetector*)createLineSegmentDetector:(int)refine scale:(double)scale sigma_scale:(double)sigma_scale quant:(double)quant ang_th:(double)ang_th NS_SWIFT_NAME(createLineSegmentDetector(refine:scale:sigma_scale:quant:ang_th:)); /** * Creates a smart pointer to a LineSegmentDetector object and initializes it. * * The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want * to edit those, as to tailor it for their own application. * * @param refine The way found lines will be refined, see #LineSegmentDetectorModes * @param scale The scale of the image that will be used to find the lines. Range (0..1]. * @param sigma_scale Sigma for Gaussian filter. It is computed as sigma = sigma_scale/scale. * @param quant Bound to the quantization error on the gradient norm. */ + (LineSegmentDetector*)createLineSegmentDetector:(int)refine scale:(double)scale sigma_scale:(double)sigma_scale quant:(double)quant NS_SWIFT_NAME(createLineSegmentDetector(refine:scale:sigma_scale:quant:)); /** * Creates a smart pointer to a LineSegmentDetector object and initializes it. * * The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want * to edit those, as to tailor it for their own application. * * @param refine The way found lines will be refined, see #LineSegmentDetectorModes * @param scale The scale of the image that will be used to find the lines. Range (0..1]. * @param sigma_scale Sigma for Gaussian filter. It is computed as sigma = sigma_scale/scale. */ + (LineSegmentDetector*)createLineSegmentDetector:(int)refine scale:(double)scale sigma_scale:(double)sigma_scale NS_SWIFT_NAME(createLineSegmentDetector(refine:scale:sigma_scale:)); /** * Creates a smart pointer to a LineSegmentDetector object and initializes it. * * The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want * to edit those, as to tailor it for their own application. * * @param refine The way found lines will be refined, see #LineSegmentDetectorModes * @param scale The scale of the image that will be used to find the lines. Range (0..1]. */ + (LineSegmentDetector*)createLineSegmentDetector:(int)refine scale:(double)scale NS_SWIFT_NAME(createLineSegmentDetector(refine:scale:)); /** * Creates a smart pointer to a LineSegmentDetector object and initializes it. * * The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want * to edit those, as to tailor it for their own application. * * @param refine The way found lines will be refined, see #LineSegmentDetectorModes */ + (LineSegmentDetector*)createLineSegmentDetector:(int)refine NS_SWIFT_NAME(createLineSegmentDetector(refine:)); /** * Creates a smart pointer to a LineSegmentDetector object and initializes it. * * The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want * to edit those, as to tailor it for their own application. * */ + (LineSegmentDetector*)createLineSegmentDetector NS_SWIFT_NAME(createLineSegmentDetector()); // // Mat cv::getGaussianKernel(int ksize, double sigma, int ktype = CV_64F) // /** * Returns Gaussian filter coefficients. * * The function computes and returns the `$$\texttt{ksize} \times 1$$` matrix of Gaussian filter * coefficients: * * `$$G_i= \alpha *e^{-(i-( \texttt{ksize} -1)/2)^2/(2* \texttt{sigma}^2)},$$` * * where `$$i=0..\texttt{ksize}-1$$` and `$$\alpha$$` is the scale factor chosen so that `$$\sum_i G_i=1$$`. * * Two of such generated kernels can be passed to sepFilter2D. Those functions automatically recognize * smoothing kernels (a symmetrical kernel with sum of weights equal to 1) and handle them accordingly. * You may also use the higher-level GaussianBlur. * @param ksize Aperture size. It should be odd ( `$$\texttt{ksize} \mod 2 = 1$$` ) and positive. * @param sigma Gaussian standard deviation. If it is non-positive, it is computed from ksize as * `sigma = 0.3*((ksize-1)*0.5 - 1) + 0.8`. * @param ktype Type of filter coefficients. It can be CV_32F or CV_64F . * @see `+sepFilter2D:dst:ddepth:kernelX:kernelY:anchor:delta:borderType:`, `+getDerivKernels:ky:dx:dy:ksize:normalize:ktype:`, `+getStructuringElement:ksize:anchor:`, `+GaussianBlur:dst:ksize:sigmaX:sigmaY:borderType:` */ + (Mat*)getGaussianKernel:(int)ksize sigma:(double)sigma ktype:(int)ktype NS_SWIFT_NAME(getGaussianKernel(ksize:sigma:ktype:)); /** * Returns Gaussian filter coefficients. * * The function computes and returns the `$$\texttt{ksize} \times 1$$` matrix of Gaussian filter * coefficients: * * `$$G_i= \alpha *e^{-(i-( \texttt{ksize} -1)/2)^2/(2* \texttt{sigma}^2)},$$` * * where `$$i=0..\texttt{ksize}-1$$` and `$$\alpha$$` is the scale factor chosen so that `$$\sum_i G_i=1$$`. * * Two of such generated kernels can be passed to sepFilter2D. Those functions automatically recognize * smoothing kernels (a symmetrical kernel with sum of weights equal to 1) and handle them accordingly. * You may also use the higher-level GaussianBlur. * @param ksize Aperture size. It should be odd ( `$$\texttt{ksize} \mod 2 = 1$$` ) and positive. * @param sigma Gaussian standard deviation. If it is non-positive, it is computed from ksize as * `sigma = 0.3*((ksize-1)*0.5 - 1) + 0.8`. * @see `+sepFilter2D:dst:ddepth:kernelX:kernelY:anchor:delta:borderType:`, `+getDerivKernels:ky:dx:dy:ksize:normalize:ktype:`, `+getStructuringElement:ksize:anchor:`, `+GaussianBlur:dst:ksize:sigmaX:sigmaY:borderType:` */ + (Mat*)getGaussianKernel:(int)ksize sigma:(double)sigma NS_SWIFT_NAME(getGaussianKernel(ksize:sigma:)); // // void cv::getDerivKernels(Mat& kx, Mat& ky, int dx, int dy, int ksize, bool normalize = false, int ktype = CV_32F) // /** * Returns filter coefficients for computing spatial image derivatives. * * The function computes and returns the filter coefficients for spatial image derivatives. When * `ksize=FILTER_SCHARR`, the Scharr `$$3 \times 3$$` kernels are generated (see #Scharr). Otherwise, Sobel * kernels are generated (see #Sobel). The filters are normally passed to #sepFilter2D or to * * @param kx Output matrix of row filter coefficients. It has the type ktype . * @param ky Output matrix of column filter coefficients. It has the type ktype . * @param dx Derivative order in respect of x. * @param dy Derivative order in respect of y. * @param ksize Aperture size. It can be FILTER_SCHARR, 1, 3, 5, or 7. * @param normalize Flag indicating whether to normalize (scale down) the filter coefficients or not. * Theoretically, the coefficients should have the denominator `$$=2^{ksize*2-dx-dy-2}$$`. If you are * going to filter floating-point images, you are likely to use the normalized kernels. But if you * compute derivatives of an 8-bit image, store the results in a 16-bit image, and wish to preserve * all the fractional bits, you may want to set normalize=false . * @param ktype Type of filter coefficients. It can be CV_32f or CV_64F . */ + (void)getDerivKernels:(Mat*)kx ky:(Mat*)ky dx:(int)dx dy:(int)dy ksize:(int)ksize normalize:(BOOL)normalize ktype:(int)ktype NS_SWIFT_NAME(getDerivKernels(kx:ky:dx:dy:ksize:normalize:ktype:)); /** * Returns filter coefficients for computing spatial image derivatives. * * The function computes and returns the filter coefficients for spatial image derivatives. When * `ksize=FILTER_SCHARR`, the Scharr `$$3 \times 3$$` kernels are generated (see #Scharr). Otherwise, Sobel * kernels are generated (see #Sobel). The filters are normally passed to #sepFilter2D or to * * @param kx Output matrix of row filter coefficients. It has the type ktype . * @param ky Output matrix of column filter coefficients. It has the type ktype . * @param dx Derivative order in respect of x. * @param dy Derivative order in respect of y. * @param ksize Aperture size. It can be FILTER_SCHARR, 1, 3, 5, or 7. * @param normalize Flag indicating whether to normalize (scale down) the filter coefficients or not. * Theoretically, the coefficients should have the denominator `$$=2^{ksize*2-dx-dy-2}$$`. If you are * going to filter floating-point images, you are likely to use the normalized kernels. But if you * compute derivatives of an 8-bit image, store the results in a 16-bit image, and wish to preserve * all the fractional bits, you may want to set normalize=false . */ + (void)getDerivKernels:(Mat*)kx ky:(Mat*)ky dx:(int)dx dy:(int)dy ksize:(int)ksize normalize:(BOOL)normalize NS_SWIFT_NAME(getDerivKernels(kx:ky:dx:dy:ksize:normalize:)); /** * Returns filter coefficients for computing spatial image derivatives. * * The function computes and returns the filter coefficients for spatial image derivatives. When * `ksize=FILTER_SCHARR`, the Scharr `$$3 \times 3$$` kernels are generated (see #Scharr). Otherwise, Sobel * kernels are generated (see #Sobel). The filters are normally passed to #sepFilter2D or to * * @param kx Output matrix of row filter coefficients. It has the type ktype . * @param ky Output matrix of column filter coefficients. It has the type ktype . * @param dx Derivative order in respect of x. * @param dy Derivative order in respect of y. * @param ksize Aperture size. It can be FILTER_SCHARR, 1, 3, 5, or 7. * Theoretically, the coefficients should have the denominator `$$=2^{ksize*2-dx-dy-2}$$`. If you are * going to filter floating-point images, you are likely to use the normalized kernels. But if you * compute derivatives of an 8-bit image, store the results in a 16-bit image, and wish to preserve * all the fractional bits, you may want to set normalize=false . */ + (void)getDerivKernels:(Mat*)kx ky:(Mat*)ky dx:(int)dx dy:(int)dy ksize:(int)ksize NS_SWIFT_NAME(getDerivKernels(kx:ky:dx:dy:ksize:)); // // Mat cv::getGaborKernel(Size ksize, double sigma, double theta, double lambd, double gamma, double psi = CV_PI*0.5, int ktype = CV_64F) // /** * Returns Gabor filter coefficients. * * For more details about gabor filter equations and parameters, see: [Gabor * Filter](http://en.wikipedia.org/wiki/Gabor_filter). * * @param ksize Size of the filter returned. * @param sigma Standard deviation of the gaussian envelope. * @param theta Orientation of the normal to the parallel stripes of a Gabor function. * @param lambd Wavelength of the sinusoidal factor. * @param gamma Spatial aspect ratio. * @param psi Phase offset. * @param ktype Type of filter coefficients. It can be CV_32F or CV_64F . */ + (Mat*)getGaborKernel:(Size2i*)ksize sigma:(double)sigma theta:(double)theta lambd:(double)lambd gamma:(double)gamma psi:(double)psi ktype:(int)ktype NS_SWIFT_NAME(getGaborKernel(ksize:sigma:theta:lambd:gamma:psi:ktype:)); /** * Returns Gabor filter coefficients. * * For more details about gabor filter equations and parameters, see: [Gabor * Filter](http://en.wikipedia.org/wiki/Gabor_filter). * * @param ksize Size of the filter returned. * @param sigma Standard deviation of the gaussian envelope. * @param theta Orientation of the normal to the parallel stripes of a Gabor function. * @param lambd Wavelength of the sinusoidal factor. * @param gamma Spatial aspect ratio. * @param psi Phase offset. */ + (Mat*)getGaborKernel:(Size2i*)ksize sigma:(double)sigma theta:(double)theta lambd:(double)lambd gamma:(double)gamma psi:(double)psi NS_SWIFT_NAME(getGaborKernel(ksize:sigma:theta:lambd:gamma:psi:)); /** * Returns Gabor filter coefficients. * * For more details about gabor filter equations and parameters, see: [Gabor * Filter](http://en.wikipedia.org/wiki/Gabor_filter). * * @param ksize Size of the filter returned. * @param sigma Standard deviation of the gaussian envelope. * @param theta Orientation of the normal to the parallel stripes of a Gabor function. * @param lambd Wavelength of the sinusoidal factor. * @param gamma Spatial aspect ratio. */ + (Mat*)getGaborKernel:(Size2i*)ksize sigma:(double)sigma theta:(double)theta lambd:(double)lambd gamma:(double)gamma NS_SWIFT_NAME(getGaborKernel(ksize:sigma:theta:lambd:gamma:)); // // Mat cv::getStructuringElement(MorphShapes shape, Size ksize, Point anchor = Point(-1,-1)) // /** * Returns a structuring element of the specified size and shape for morphological operations. * * The function constructs and returns the structuring element that can be further passed to #erode, * #dilate or #morphologyEx. But you can also construct an arbitrary binary mask yourself and use it as * the structuring element. * * @param shape Element shape that could be one of #MorphShapes * @param ksize Size of the structuring element. * @param anchor Anchor position within the element. The default value `$$(-1, -1)$$` means that the * anchor is at the center. Note that only the shape of a cross-shaped element depends on the anchor * position. In other cases the anchor just regulates how much the result of the morphological * operation is shifted. */ + (Mat*)getStructuringElement:(MorphShapes)shape ksize:(Size2i*)ksize anchor:(Point2i*)anchor NS_SWIFT_NAME(getStructuringElement(shape:ksize:anchor:)); /** * Returns a structuring element of the specified size and shape for morphological operations. * * The function constructs and returns the structuring element that can be further passed to #erode, * #dilate or #morphologyEx. But you can also construct an arbitrary binary mask yourself and use it as * the structuring element. * * @param shape Element shape that could be one of #MorphShapes * @param ksize Size of the structuring element. * anchor is at the center. Note that only the shape of a cross-shaped element depends on the anchor * position. In other cases the anchor just regulates how much the result of the morphological * operation is shifted. */ + (Mat*)getStructuringElement:(MorphShapes)shape ksize:(Size2i*)ksize NS_SWIFT_NAME(getStructuringElement(shape:ksize:)); // // void cv::medianBlur(Mat src, Mat& dst, int ksize) // /** * Blurs an image using the median filter. * * The function smoothes an image using the median filter with the `$$\texttt{ksize} \times * \texttt{ksize}$$` aperture. Each channel of a multi-channel image is processed independently. * In-place operation is supported. * * NOTE: The median filter uses #BORDER_REPLICATE internally to cope with border pixels, see #BorderTypes * * @param src input 1-, 3-, or 4-channel image; when ksize is 3 or 5, the image depth should be * CV_8U, CV_16U, or CV_32F, for larger aperture sizes, it can only be CV_8U. * @param dst destination array of the same size and type as src. * @param ksize aperture linear size; it must be odd and greater than 1, for example: 3, 5, 7 ... * @see `+bilateralFilter:dst:d:sigmaColor:sigmaSpace:borderType:`, `+blur:dst:ksize:anchor:borderType:`, `+boxFilter:dst:ddepth:ksize:anchor:normalize:borderType:`, `+GaussianBlur:dst:ksize:sigmaX:sigmaY:borderType:` */ + (void)medianBlur:(Mat*)src dst:(Mat*)dst ksize:(int)ksize NS_SWIFT_NAME(medianBlur(src:dst:ksize:)); // // void cv::GaussianBlur(Mat src, Mat& dst, Size ksize, double sigmaX, double sigmaY = 0, BorderTypes borderType = BORDER_DEFAULT) // /** * Blurs an image using a Gaussian filter. * * The function convolves the source image with the specified Gaussian kernel. In-place filtering is * supported. * * @param src input image; the image can have any number of channels, which are processed * independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. * @param dst output image of the same size and type as src. * @param ksize Gaussian kernel size. ksize.width and ksize.height can differ but they both must be * positive and odd. Or, they can be zero's and then they are computed from sigma. * @param sigmaX Gaussian kernel standard deviation in X direction. * @param sigmaY Gaussian kernel standard deviation in Y direction; if sigmaY is zero, it is set to be * equal to sigmaX, if both sigmas are zeros, they are computed from ksize.width and ksize.height, * respectively (see #getGaussianKernel for details); to fully control the result regardless of * possible future modifications of all this semantics, it is recommended to specify all of ksize, * sigmaX, and sigmaY. * @param borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported. * * @see `+sepFilter2D:dst:ddepth:kernelX:kernelY:anchor:delta:borderType:`, `+filter2D:dst:ddepth:kernel:anchor:delta:borderType:`, `+blur:dst:ksize:anchor:borderType:`, `+boxFilter:dst:ddepth:ksize:anchor:normalize:borderType:`, `+bilateralFilter:dst:d:sigmaColor:sigmaSpace:borderType:`, `+medianBlur:dst:ksize:` */ + (void)GaussianBlur:(Mat*)src dst:(Mat*)dst ksize:(Size2i*)ksize sigmaX:(double)sigmaX sigmaY:(double)sigmaY borderType:(BorderTypes)borderType NS_SWIFT_NAME(GaussianBlur(src:dst:ksize:sigmaX:sigmaY:borderType:)); /** * Blurs an image using a Gaussian filter. * * The function convolves the source image with the specified Gaussian kernel. In-place filtering is * supported. * * @param src input image; the image can have any number of channels, which are processed * independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. * @param dst output image of the same size and type as src. * @param ksize Gaussian kernel size. ksize.width and ksize.height can differ but they both must be * positive and odd. Or, they can be zero's and then they are computed from sigma. * @param sigmaX Gaussian kernel standard deviation in X direction. * @param sigmaY Gaussian kernel standard deviation in Y direction; if sigmaY is zero, it is set to be * equal to sigmaX, if both sigmas are zeros, they are computed from ksize.width and ksize.height, * respectively (see #getGaussianKernel for details); to fully control the result regardless of * possible future modifications of all this semantics, it is recommended to specify all of ksize, * sigmaX, and sigmaY. * * @see `+sepFilter2D:dst:ddepth:kernelX:kernelY:anchor:delta:borderType:`, `+filter2D:dst:ddepth:kernel:anchor:delta:borderType:`, `+blur:dst:ksize:anchor:borderType:`, `+boxFilter:dst:ddepth:ksize:anchor:normalize:borderType:`, `+bilateralFilter:dst:d:sigmaColor:sigmaSpace:borderType:`, `+medianBlur:dst:ksize:` */ + (void)GaussianBlur:(Mat*)src dst:(Mat*)dst ksize:(Size2i*)ksize sigmaX:(double)sigmaX sigmaY:(double)sigmaY NS_SWIFT_NAME(GaussianBlur(src:dst:ksize:sigmaX:sigmaY:)); /** * Blurs an image using a Gaussian filter. * * The function convolves the source image with the specified Gaussian kernel. In-place filtering is * supported. * * @param src input image; the image can have any number of channels, which are processed * independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. * @param dst output image of the same size and type as src. * @param ksize Gaussian kernel size. ksize.width and ksize.height can differ but they both must be * positive and odd. Or, they can be zero's and then they are computed from sigma. * @param sigmaX Gaussian kernel standard deviation in X direction. * equal to sigmaX, if both sigmas are zeros, they are computed from ksize.width and ksize.height, * respectively (see #getGaussianKernel for details); to fully control the result regardless of * possible future modifications of all this semantics, it is recommended to specify all of ksize, * sigmaX, and sigmaY. * * @see `+sepFilter2D:dst:ddepth:kernelX:kernelY:anchor:delta:borderType:`, `+filter2D:dst:ddepth:kernel:anchor:delta:borderType:`, `+blur:dst:ksize:anchor:borderType:`, `+boxFilter:dst:ddepth:ksize:anchor:normalize:borderType:`, `+bilateralFilter:dst:d:sigmaColor:sigmaSpace:borderType:`, `+medianBlur:dst:ksize:` */ + (void)GaussianBlur:(Mat*)src dst:(Mat*)dst ksize:(Size2i*)ksize sigmaX:(double)sigmaX NS_SWIFT_NAME(GaussianBlur(src:dst:ksize:sigmaX:)); // // void cv::bilateralFilter(Mat src, Mat& dst, int d, double sigmaColor, double sigmaSpace, BorderTypes borderType = BORDER_DEFAULT) // /** * Applies the bilateral filter to an image. * * The function applies bilateral filtering to the input image, as described in * http://www.dai.ed.ac.uk/CVonline/LOCAL_COPIES/MANDUCHI1/Bilateral_Filtering.html * bilateralFilter can reduce unwanted noise very well while keeping edges fairly sharp. However, it is * very slow compared to most filters. * * _Sigma values_: For simplicity, you can set the 2 sigma values to be the same. If they are small (\< * 10), the filter will not have much effect, whereas if they are large (\> 150), they will have a very * strong effect, making the image look "cartoonish". * * _Filter size_: Large filters (d \> 5) are very slow, so it is recommended to use d=5 for real-time * applications, and perhaps d=9 for offline applications that need heavy noise filtering. * * This filter does not work inplace. * @param src Source 8-bit or floating-point, 1-channel or 3-channel image. * @param dst Destination image of the same size and type as src . * @param d Diameter of each pixel neighborhood that is used during filtering. If it is non-positive, * it is computed from sigmaSpace. * @param sigmaColor Filter sigma in the color space. A larger value of the parameter means that * farther colors within the pixel neighborhood (see sigmaSpace) will be mixed together, resulting * in larger areas of semi-equal color. * @param sigmaSpace Filter sigma in the coordinate space. A larger value of the parameter means that * farther pixels will influence each other as long as their colors are close enough (see sigmaColor * ). When d\>0, it specifies the neighborhood size regardless of sigmaSpace. Otherwise, d is * proportional to sigmaSpace. * @param borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes */ + (void)bilateralFilter:(Mat*)src dst:(Mat*)dst d:(int)d sigmaColor:(double)sigmaColor sigmaSpace:(double)sigmaSpace borderType:(BorderTypes)borderType NS_SWIFT_NAME(bilateralFilter(src:dst:d:sigmaColor:sigmaSpace:borderType:)); /** * Applies the bilateral filter to an image. * * The function applies bilateral filtering to the input image, as described in * http://www.dai.ed.ac.uk/CVonline/LOCAL_COPIES/MANDUCHI1/Bilateral_Filtering.html * bilateralFilter can reduce unwanted noise very well while keeping edges fairly sharp. However, it is * very slow compared to most filters. * * _Sigma values_: For simplicity, you can set the 2 sigma values to be the same. If they are small (\< * 10), the filter will not have much effect, whereas if they are large (\> 150), they will have a very * strong effect, making the image look "cartoonish". * * _Filter size_: Large filters (d \> 5) are very slow, so it is recommended to use d=5 for real-time * applications, and perhaps d=9 for offline applications that need heavy noise filtering. * * This filter does not work inplace. * @param src Source 8-bit or floating-point, 1-channel or 3-channel image. * @param dst Destination image of the same size and type as src . * @param d Diameter of each pixel neighborhood that is used during filtering. If it is non-positive, * it is computed from sigmaSpace. * @param sigmaColor Filter sigma in the color space. A larger value of the parameter means that * farther colors within the pixel neighborhood (see sigmaSpace) will be mixed together, resulting * in larger areas of semi-equal color. * @param sigmaSpace Filter sigma in the coordinate space. A larger value of the parameter means that * farther pixels will influence each other as long as their colors are close enough (see sigmaColor * ). When d\>0, it specifies the neighborhood size regardless of sigmaSpace. Otherwise, d is * proportional to sigmaSpace. */ + (void)bilateralFilter:(Mat*)src dst:(Mat*)dst d:(int)d sigmaColor:(double)sigmaColor sigmaSpace:(double)sigmaSpace NS_SWIFT_NAME(bilateralFilter(src:dst:d:sigmaColor:sigmaSpace:)); // // void cv::boxFilter(Mat src, Mat& dst, int ddepth, Size ksize, Point anchor = Point(-1,-1), bool normalize = true, BorderTypes borderType = BORDER_DEFAULT) // /** * Blurs an image using the box filter. * * The function smooths an image using the kernel: * * `$$\texttt{K} = \alpha \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \end{bmatrix}$$` * * where * * `$$\alpha = \begin{cases} \frac{1}{\texttt{ksize.width*ksize.height}} & \texttt{when } \texttt{normalize=true} \\1 & \texttt{otherwise}\end{cases}$$` * * Unnormalized box filter is useful for computing various integral characteristics over each pixel * neighborhood, such as covariance matrices of image derivatives (used in dense optical flow * algorithms, and so on). If you need to compute pixel sums over variable-size windows, use #integral. * * @param src input image. * @param dst output image of the same size and type as src. * @param ddepth the output image depth (-1 to use src.depth()). * @param ksize blurring kernel size. * @param anchor anchor point; default value Point(-1,-1) means that the anchor is at the kernel * center. * @param normalize flag, specifying whether the kernel is normalized by its area or not. * @param borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported. * @see `+blur:dst:ksize:anchor:borderType:`, `+bilateralFilter:dst:d:sigmaColor:sigmaSpace:borderType:`, `+GaussianBlur:dst:ksize:sigmaX:sigmaY:borderType:`, `+medianBlur:dst:ksize:`, `+integral:sum:sdepth:` */ + (void)boxFilter:(Mat*)src dst:(Mat*)dst ddepth:(int)ddepth ksize:(Size2i*)ksize anchor:(Point2i*)anchor normalize:(BOOL)normalize borderType:(BorderTypes)borderType NS_SWIFT_NAME(boxFilter(src:dst:ddepth:ksize:anchor:normalize:borderType:)); /** * Blurs an image using the box filter. * * The function smooths an image using the kernel: * * `$$\texttt{K} = \alpha \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \end{bmatrix}$$` * * where * * `$$\alpha = \begin{cases} \frac{1}{\texttt{ksize.width*ksize.height}} & \texttt{when } \texttt{normalize=true} \\1 & \texttt{otherwise}\end{cases}$$` * * Unnormalized box filter is useful for computing various integral characteristics over each pixel * neighborhood, such as covariance matrices of image derivatives (used in dense optical flow * algorithms, and so on). If you need to compute pixel sums over variable-size windows, use #integral. * * @param src input image. * @param dst output image of the same size and type as src. * @param ddepth the output image depth (-1 to use src.depth()). * @param ksize blurring kernel size. * @param anchor anchor point; default value Point(-1,-1) means that the anchor is at the kernel * center. * @param normalize flag, specifying whether the kernel is normalized by its area or not. * @see `+blur:dst:ksize:anchor:borderType:`, `+bilateralFilter:dst:d:sigmaColor:sigmaSpace:borderType:`, `+GaussianBlur:dst:ksize:sigmaX:sigmaY:borderType:`, `+medianBlur:dst:ksize:`, `+integral:sum:sdepth:` */ + (void)boxFilter:(Mat*)src dst:(Mat*)dst ddepth:(int)ddepth ksize:(Size2i*)ksize anchor:(Point2i*)anchor normalize:(BOOL)normalize NS_SWIFT_NAME(boxFilter(src:dst:ddepth:ksize:anchor:normalize:)); /** * Blurs an image using the box filter. * * The function smooths an image using the kernel: * * `$$\texttt{K} = \alpha \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \end{bmatrix}$$` * * where * * `$$\alpha = \begin{cases} \frac{1}{\texttt{ksize.width*ksize.height}} & \texttt{when } \texttt{normalize=true} \\1 & \texttt{otherwise}\end{cases}$$` * * Unnormalized box filter is useful for computing various integral characteristics over each pixel * neighborhood, such as covariance matrices of image derivatives (used in dense optical flow * algorithms, and so on). If you need to compute pixel sums over variable-size windows, use #integral. * * @param src input image. * @param dst output image of the same size and type as src. * @param ddepth the output image depth (-1 to use src.depth()). * @param ksize blurring kernel size. * @param anchor anchor point; default value Point(-1,-1) means that the anchor is at the kernel * center. * @see `+blur:dst:ksize:anchor:borderType:`, `+bilateralFilter:dst:d:sigmaColor:sigmaSpace:borderType:`, `+GaussianBlur:dst:ksize:sigmaX:sigmaY:borderType:`, `+medianBlur:dst:ksize:`, `+integral:sum:sdepth:` */ + (void)boxFilter:(Mat*)src dst:(Mat*)dst ddepth:(int)ddepth ksize:(Size2i*)ksize anchor:(Point2i*)anchor NS_SWIFT_NAME(boxFilter(src:dst:ddepth:ksize:anchor:)); /** * Blurs an image using the box filter. * * The function smooths an image using the kernel: * * `$$\texttt{K} = \alpha \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \end{bmatrix}$$` * * where * * `$$\alpha = \begin{cases} \frac{1}{\texttt{ksize.width*ksize.height}} & \texttt{when } \texttt{normalize=true} \\1 & \texttt{otherwise}\end{cases}$$` * * Unnormalized box filter is useful for computing various integral characteristics over each pixel * neighborhood, such as covariance matrices of image derivatives (used in dense optical flow * algorithms, and so on). If you need to compute pixel sums over variable-size windows, use #integral. * * @param src input image. * @param dst output image of the same size and type as src. * @param ddepth the output image depth (-1 to use src.depth()). * @param ksize blurring kernel size. * center. * @see `+blur:dst:ksize:anchor:borderType:`, `+bilateralFilter:dst:d:sigmaColor:sigmaSpace:borderType:`, `+GaussianBlur:dst:ksize:sigmaX:sigmaY:borderType:`, `+medianBlur:dst:ksize:`, `+integral:sum:sdepth:` */ + (void)boxFilter:(Mat*)src dst:(Mat*)dst ddepth:(int)ddepth ksize:(Size2i*)ksize NS_SWIFT_NAME(boxFilter(src:dst:ddepth:ksize:)); // // void cv::sqrBoxFilter(Mat src, Mat& dst, int ddepth, Size ksize, Point anchor = Point(-1, -1), bool normalize = true, BorderTypes borderType = BORDER_DEFAULT) // /** * Calculates the normalized sum of squares of the pixel values overlapping the filter. * * For every pixel `$$ (x, y) $$` in the source image, the function calculates the sum of squares of those neighboring * pixel values which overlap the filter placed over the pixel `$$ (x, y) $$`. * * The unnormalized square box filter can be useful in computing local image statistics such as the local * variance and standard deviation around the neighborhood of a pixel. * * @param src input image * @param dst output image of the same size and type as src * @param ddepth the output image depth (-1 to use src.depth()) * @param ksize kernel size * @param anchor kernel anchor point. The default value of Point(-1, -1) denotes that the anchor is at the kernel * center. * @param normalize flag, specifying whether the kernel is to be normalized by it's area or not. * @param borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported. * @see `+boxFilter:dst:ddepth:ksize:anchor:normalize:borderType:` */ + (void)sqrBoxFilter:(Mat*)src dst:(Mat*)dst ddepth:(int)ddepth ksize:(Size2i*)ksize anchor:(Point2i*)anchor normalize:(BOOL)normalize borderType:(BorderTypes)borderType NS_SWIFT_NAME(sqrBoxFilter(src:dst:ddepth:ksize:anchor:normalize:borderType:)); /** * Calculates the normalized sum of squares of the pixel values overlapping the filter. * * For every pixel `$$ (x, y) $$` in the source image, the function calculates the sum of squares of those neighboring * pixel values which overlap the filter placed over the pixel `$$ (x, y) $$`. * * The unnormalized square box filter can be useful in computing local image statistics such as the local * variance and standard deviation around the neighborhood of a pixel. * * @param src input image * @param dst output image of the same size and type as src * @param ddepth the output image depth (-1 to use src.depth()) * @param ksize kernel size * @param anchor kernel anchor point. The default value of Point(-1, -1) denotes that the anchor is at the kernel * center. * @param normalize flag, specifying whether the kernel is to be normalized by it's area or not. * @see `+boxFilter:dst:ddepth:ksize:anchor:normalize:borderType:` */ + (void)sqrBoxFilter:(Mat*)src dst:(Mat*)dst ddepth:(int)ddepth ksize:(Size2i*)ksize anchor:(Point2i*)anchor normalize:(BOOL)normalize NS_SWIFT_NAME(sqrBoxFilter(src:dst:ddepth:ksize:anchor:normalize:)); /** * Calculates the normalized sum of squares of the pixel values overlapping the filter. * * For every pixel `$$ (x, y) $$` in the source image, the function calculates the sum of squares of those neighboring * pixel values which overlap the filter placed over the pixel `$$ (x, y) $$`. * * The unnormalized square box filter can be useful in computing local image statistics such as the local * variance and standard deviation around the neighborhood of a pixel. * * @param src input image * @param dst output image of the same size and type as src * @param ddepth the output image depth (-1 to use src.depth()) * @param ksize kernel size * @param anchor kernel anchor point. The default value of Point(-1, -1) denotes that the anchor is at the kernel * center. * @see `+boxFilter:dst:ddepth:ksize:anchor:normalize:borderType:` */ + (void)sqrBoxFilter:(Mat*)src dst:(Mat*)dst ddepth:(int)ddepth ksize:(Size2i*)ksize anchor:(Point2i*)anchor NS_SWIFT_NAME(sqrBoxFilter(src:dst:ddepth:ksize:anchor:)); /** * Calculates the normalized sum of squares of the pixel values overlapping the filter. * * For every pixel `$$ (x, y) $$` in the source image, the function calculates the sum of squares of those neighboring * pixel values which overlap the filter placed over the pixel `$$ (x, y) $$`. * * The unnormalized square box filter can be useful in computing local image statistics such as the local * variance and standard deviation around the neighborhood of a pixel. * * @param src input image * @param dst output image of the same size and type as src * @param ddepth the output image depth (-1 to use src.depth()) * @param ksize kernel size * center. * @see `+boxFilter:dst:ddepth:ksize:anchor:normalize:borderType:` */ + (void)sqrBoxFilter:(Mat*)src dst:(Mat*)dst ddepth:(int)ddepth ksize:(Size2i*)ksize NS_SWIFT_NAME(sqrBoxFilter(src:dst:ddepth:ksize:)); // // void cv::blur(Mat src, Mat& dst, Size ksize, Point anchor = Point(-1,-1), BorderTypes borderType = BORDER_DEFAULT) // /** * Blurs an image using the normalized box filter. * * The function smooths an image using the kernel: * * `$$\texttt{K} = \frac{1}{\texttt{ksize.width*ksize.height}} \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \end{bmatrix}$$` * * The call `blur(src, dst, ksize, anchor, borderType)` is equivalent to `boxFilter(src, dst, src.type(), ksize, * anchor, true, borderType)`. * * @param src input image; it can have any number of channels, which are processed independently, but * the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. * @param dst output image of the same size and type as src. * @param ksize blurring kernel size. * @param anchor anchor point; default value Point(-1,-1) means that the anchor is at the kernel * center. * @param borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported. * @see `+boxFilter:dst:ddepth:ksize:anchor:normalize:borderType:`, `+bilateralFilter:dst:d:sigmaColor:sigmaSpace:borderType:`, `+GaussianBlur:dst:ksize:sigmaX:sigmaY:borderType:`, `+medianBlur:dst:ksize:` */ + (void)blur:(Mat*)src dst:(Mat*)dst ksize:(Size2i*)ksize anchor:(Point2i*)anchor borderType:(BorderTypes)borderType NS_SWIFT_NAME(blur(src:dst:ksize:anchor:borderType:)); /** * Blurs an image using the normalized box filter. * * The function smooths an image using the kernel: * * `$$\texttt{K} = \frac{1}{\texttt{ksize.width*ksize.height}} \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \end{bmatrix}$$` * * The call `blur(src, dst, ksize, anchor, borderType)` is equivalent to `boxFilter(src, dst, src.type(), ksize, * anchor, true, borderType)`. * * @param src input image; it can have any number of channels, which are processed independently, but * the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. * @param dst output image of the same size and type as src. * @param ksize blurring kernel size. * @param anchor anchor point; default value Point(-1,-1) means that the anchor is at the kernel * center. * @see `+boxFilter:dst:ddepth:ksize:anchor:normalize:borderType:`, `+bilateralFilter:dst:d:sigmaColor:sigmaSpace:borderType:`, `+GaussianBlur:dst:ksize:sigmaX:sigmaY:borderType:`, `+medianBlur:dst:ksize:` */ + (void)blur:(Mat*)src dst:(Mat*)dst ksize:(Size2i*)ksize anchor:(Point2i*)anchor NS_SWIFT_NAME(blur(src:dst:ksize:anchor:)); /** * Blurs an image using the normalized box filter. * * The function smooths an image using the kernel: * * `$$\texttt{K} = \frac{1}{\texttt{ksize.width*ksize.height}} \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \end{bmatrix}$$` * * The call `blur(src, dst, ksize, anchor, borderType)` is equivalent to `boxFilter(src, dst, src.type(), ksize, * anchor, true, borderType)`. * * @param src input image; it can have any number of channels, which are processed independently, but * the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. * @param dst output image of the same size and type as src. * @param ksize blurring kernel size. * center. * @see `+boxFilter:dst:ddepth:ksize:anchor:normalize:borderType:`, `+bilateralFilter:dst:d:sigmaColor:sigmaSpace:borderType:`, `+GaussianBlur:dst:ksize:sigmaX:sigmaY:borderType:`, `+medianBlur:dst:ksize:` */ + (void)blur:(Mat*)src dst:(Mat*)dst ksize:(Size2i*)ksize NS_SWIFT_NAME(blur(src:dst:ksize:)); // // void cv::filter2D(Mat src, Mat& dst, int ddepth, Mat kernel, Point anchor = Point(-1,-1), double delta = 0, BorderTypes borderType = BORDER_DEFAULT) // /** * Convolves an image with the kernel. * * The function applies an arbitrary linear filter to an image. In-place operation is supported. When * the aperture is partially outside the image, the function interpolates outlier pixel values * according to the specified border mode. * * The function does actually compute correlation, not the convolution: * * `$$\texttt{dst} (x,y) = \sum _{ \substack{0\leq x' < \texttt{kernel.cols}\\{0\leq y' < \texttt{kernel.rows}}}} \texttt{kernel} (x',y')* \texttt{src} (x+x'- \texttt{anchor.x} ,y+y'- \texttt{anchor.y} )$$` * * That is, the kernel is not mirrored around the anchor point. If you need a real convolution, flip * the kernel using #flip and set the new anchor to `(kernel.cols - anchor.x - 1, kernel.rows - * anchor.y - 1)`. * * The function uses the DFT-based algorithm in case of sufficiently large kernels (~`11 x 11` or * larger) and the direct algorithm for small kernels. * * @param src input image. * @param dst output image of the same size and the same number of channels as src. * @param ddepth desired depth of the destination image, see REF: filter_depths "combinations" * @param kernel convolution kernel (or rather a correlation kernel), a single-channel floating point * matrix; if you want to apply different kernels to different channels, split the image into * separate color planes using split and process them individually. * @param anchor anchor of the kernel that indicates the relative position of a filtered point within * the kernel; the anchor should lie within the kernel; default value (-1,-1) means that the anchor * is at the kernel center. * @param delta optional value added to the filtered pixels before storing them in dst. * @param borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported. * @see `+sepFilter2D:dst:ddepth:kernelX:kernelY:anchor:delta:borderType:`, `dft`, `+matchTemplate:templ:result:method:mask:` */ + (void)filter2D:(Mat*)src dst:(Mat*)dst ddepth:(int)ddepth kernel:(Mat*)kernel anchor:(Point2i*)anchor delta:(double)delta borderType:(BorderTypes)borderType NS_SWIFT_NAME(filter2D(src:dst:ddepth:kernel:anchor:delta:borderType:)); /** * Convolves an image with the kernel. * * The function applies an arbitrary linear filter to an image. In-place operation is supported. When * the aperture is partially outside the image, the function interpolates outlier pixel values * according to the specified border mode. * * The function does actually compute correlation, not the convolution: * * `$$\texttt{dst} (x,y) = \sum _{ \substack{0\leq x' < \texttt{kernel.cols}\\{0\leq y' < \texttt{kernel.rows}}}} \texttt{kernel} (x',y')* \texttt{src} (x+x'- \texttt{anchor.x} ,y+y'- \texttt{anchor.y} )$$` * * That is, the kernel is not mirrored around the anchor point. If you need a real convolution, flip * the kernel using #flip and set the new anchor to `(kernel.cols - anchor.x - 1, kernel.rows - * anchor.y - 1)`. * * The function uses the DFT-based algorithm in case of sufficiently large kernels (~`11 x 11` or * larger) and the direct algorithm for small kernels. * * @param src input image. * @param dst output image of the same size and the same number of channels as src. * @param ddepth desired depth of the destination image, see REF: filter_depths "combinations" * @param kernel convolution kernel (or rather a correlation kernel), a single-channel floating point * matrix; if you want to apply different kernels to different channels, split the image into * separate color planes using split and process them individually. * @param anchor anchor of the kernel that indicates the relative position of a filtered point within * the kernel; the anchor should lie within the kernel; default value (-1,-1) means that the anchor * is at the kernel center. * @param delta optional value added to the filtered pixels before storing them in dst. * @see `+sepFilter2D:dst:ddepth:kernelX:kernelY:anchor:delta:borderType:`, `dft`, `+matchTemplate:templ:result:method:mask:` */ + (void)filter2D:(Mat*)src dst:(Mat*)dst ddepth:(int)ddepth kernel:(Mat*)kernel anchor:(Point2i*)anchor delta:(double)delta NS_SWIFT_NAME(filter2D(src:dst:ddepth:kernel:anchor:delta:)); /** * Convolves an image with the kernel. * * The function applies an arbitrary linear filter to an image. In-place operation is supported. When * the aperture is partially outside the image, the function interpolates outlier pixel values * according to the specified border mode. * * The function does actually compute correlation, not the convolution: * * `$$\texttt{dst} (x,y) = \sum _{ \substack{0\leq x' < \texttt{kernel.cols}\\{0\leq y' < \texttt{kernel.rows}}}} \texttt{kernel} (x',y')* \texttt{src} (x+x'- \texttt{anchor.x} ,y+y'- \texttt{anchor.y} )$$` * * That is, the kernel is not mirrored around the anchor point. If you need a real convolution, flip * the kernel using #flip and set the new anchor to `(kernel.cols - anchor.x - 1, kernel.rows - * anchor.y - 1)`. * * The function uses the DFT-based algorithm in case of sufficiently large kernels (~`11 x 11` or * larger) and the direct algorithm for small kernels. * * @param src input image. * @param dst output image of the same size and the same number of channels as src. * @param ddepth desired depth of the destination image, see REF: filter_depths "combinations" * @param kernel convolution kernel (or rather a correlation kernel), a single-channel floating point * matrix; if you want to apply different kernels to different channels, split the image into * separate color planes using split and process them individually. * @param anchor anchor of the kernel that indicates the relative position of a filtered point within * the kernel; the anchor should lie within the kernel; default value (-1,-1) means that the anchor * is at the kernel center. * @see `+sepFilter2D:dst:ddepth:kernelX:kernelY:anchor:delta:borderType:`, `dft`, `+matchTemplate:templ:result:method:mask:` */ + (void)filter2D:(Mat*)src dst:(Mat*)dst ddepth:(int)ddepth kernel:(Mat*)kernel anchor:(Point2i*)anchor NS_SWIFT_NAME(filter2D(src:dst:ddepth:kernel:anchor:)); /** * Convolves an image with the kernel. * * The function applies an arbitrary linear filter to an image. In-place operation is supported. When * the aperture is partially outside the image, the function interpolates outlier pixel values * according to the specified border mode. * * The function does actually compute correlation, not the convolution: * * `$$\texttt{dst} (x,y) = \sum _{ \substack{0\leq x' < \texttt{kernel.cols}\\{0\leq y' < \texttt{kernel.rows}}}} \texttt{kernel} (x',y')* \texttt{src} (x+x'- \texttt{anchor.x} ,y+y'- \texttt{anchor.y} )$$` * * That is, the kernel is not mirrored around the anchor point. If you need a real convolution, flip * the kernel using #flip and set the new anchor to `(kernel.cols - anchor.x - 1, kernel.rows - * anchor.y - 1)`. * * The function uses the DFT-based algorithm in case of sufficiently large kernels (~`11 x 11` or * larger) and the direct algorithm for small kernels. * * @param src input image. * @param dst output image of the same size and the same number of channels as src. * @param ddepth desired depth of the destination image, see REF: filter_depths "combinations" * @param kernel convolution kernel (or rather a correlation kernel), a single-channel floating point * matrix; if you want to apply different kernels to different channels, split the image into * separate color planes using split and process them individually. * the kernel; the anchor should lie within the kernel; default value (-1,-1) means that the anchor * is at the kernel center. * @see `+sepFilter2D:dst:ddepth:kernelX:kernelY:anchor:delta:borderType:`, `dft`, `+matchTemplate:templ:result:method:mask:` */ + (void)filter2D:(Mat*)src dst:(Mat*)dst ddepth:(int)ddepth kernel:(Mat*)kernel NS_SWIFT_NAME(filter2D(src:dst:ddepth:kernel:)); // // void cv::sepFilter2D(Mat src, Mat& dst, int ddepth, Mat kernelX, Mat kernelY, Point anchor = Point(-1,-1), double delta = 0, BorderTypes borderType = BORDER_DEFAULT) // /** * Applies a separable linear filter to an image. * * The function applies a separable linear filter to the image. That is, first, every row of src is * filtered with the 1D kernel kernelX. Then, every column of the result is filtered with the 1D * kernel kernelY. The final result shifted by delta is stored in dst . * * @param src Source image. * @param dst Destination image of the same size and the same number of channels as src . * @param ddepth Destination image depth, see REF: filter_depths "combinations" * @param kernelX Coefficients for filtering each row. * @param kernelY Coefficients for filtering each column. * @param anchor Anchor position within the kernel. The default value `$$(-1,-1)$$` means that the anchor * is at the kernel center. * @param delta Value added to the filtered results before storing them. * @param borderType Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported. * @see `+filter2D:dst:ddepth:kernel:anchor:delta:borderType:`, `+Sobel:dst:ddepth:dx:dy:ksize:scale:delta:borderType:`, `+GaussianBlur:dst:ksize:sigmaX:sigmaY:borderType:`, `+boxFilter:dst:ddepth:ksize:anchor:normalize:borderType:`, `+blur:dst:ksize:anchor:borderType:` */ + (void)sepFilter2D:(Mat*)src dst:(Mat*)dst ddepth:(int)ddepth kernelX:(Mat*)kernelX kernelY:(Mat*)kernelY anchor:(Point2i*)anchor delta:(double)delta borderType:(BorderTypes)borderType NS_SWIFT_NAME(sepFilter2D(src:dst:ddepth:kernelX:kernelY:anchor:delta:borderType:)); /** * Applies a separable linear filter to an image. * * The function applies a separable linear filter to the image. That is, first, every row of src is * filtered with the 1D kernel kernelX. Then, every column of the result is filtered with the 1D * kernel kernelY. The final result shifted by delta is stored in dst . * * @param src Source image. * @param dst Destination image of the same size and the same number of channels as src . * @param ddepth Destination image depth, see REF: filter_depths "combinations" * @param kernelX Coefficients for filtering each row. * @param kernelY Coefficients for filtering each column. * @param anchor Anchor position within the kernel. The default value `$$(-1,-1)$$` means that the anchor * is at the kernel center. * @param delta Value added to the filtered results before storing them. * @see `+filter2D:dst:ddepth:kernel:anchor:delta:borderType:`, `+Sobel:dst:ddepth:dx:dy:ksize:scale:delta:borderType:`, `+GaussianBlur:dst:ksize:sigmaX:sigmaY:borderType:`, `+boxFilter:dst:ddepth:ksize:anchor:normalize:borderType:`, `+blur:dst:ksize:anchor:borderType:` */ + (void)sepFilter2D:(Mat*)src dst:(Mat*)dst ddepth:(int)ddepth kernelX:(Mat*)kernelX kernelY:(Mat*)kernelY anchor:(Point2i*)anchor delta:(double)delta NS_SWIFT_NAME(sepFilter2D(src:dst:ddepth:kernelX:kernelY:anchor:delta:)); /** * Applies a separable linear filter to an image. * * The function applies a separable linear filter to the image. That is, first, every row of src is * filtered with the 1D kernel kernelX. Then, every column of the result is filtered with the 1D * kernel kernelY. The final result shifted by delta is stored in dst . * * @param src Source image. * @param dst Destination image of the same size and the same number of channels as src . * @param ddepth Destination image depth, see REF: filter_depths "combinations" * @param kernelX Coefficients for filtering each row. * @param kernelY Coefficients for filtering each column. * @param anchor Anchor position within the kernel. The default value `$$(-1,-1)$$` means that the anchor * is at the kernel center. * @see `+filter2D:dst:ddepth:kernel:anchor:delta:borderType:`, `+Sobel:dst:ddepth:dx:dy:ksize:scale:delta:borderType:`, `+GaussianBlur:dst:ksize:sigmaX:sigmaY:borderType:`, `+boxFilter:dst:ddepth:ksize:anchor:normalize:borderType:`, `+blur:dst:ksize:anchor:borderType:` */ + (void)sepFilter2D:(Mat*)src dst:(Mat*)dst ddepth:(int)ddepth kernelX:(Mat*)kernelX kernelY:(Mat*)kernelY anchor:(Point2i*)anchor NS_SWIFT_NAME(sepFilter2D(src:dst:ddepth:kernelX:kernelY:anchor:)); /** * Applies a separable linear filter to an image. * * The function applies a separable linear filter to the image. That is, first, every row of src is * filtered with the 1D kernel kernelX. Then, every column of the result is filtered with the 1D * kernel kernelY. The final result shifted by delta is stored in dst . * * @param src Source image. * @param dst Destination image of the same size and the same number of channels as src . * @param ddepth Destination image depth, see REF: filter_depths "combinations" * @param kernelX Coefficients for filtering each row. * @param kernelY Coefficients for filtering each column. * is at the kernel center. * @see `+filter2D:dst:ddepth:kernel:anchor:delta:borderType:`, `+Sobel:dst:ddepth:dx:dy:ksize:scale:delta:borderType:`, `+GaussianBlur:dst:ksize:sigmaX:sigmaY:borderType:`, `+boxFilter:dst:ddepth:ksize:anchor:normalize:borderType:`, `+blur:dst:ksize:anchor:borderType:` */ + (void)sepFilter2D:(Mat*)src dst:(Mat*)dst ddepth:(int)ddepth kernelX:(Mat*)kernelX kernelY:(Mat*)kernelY NS_SWIFT_NAME(sepFilter2D(src:dst:ddepth:kernelX:kernelY:)); // // void cv::Sobel(Mat src, Mat& dst, int ddepth, int dx, int dy, int ksize = 3, double scale = 1, double delta = 0, BorderTypes borderType = BORDER_DEFAULT) // /** * Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator. * * In all cases except one, the `$$\texttt{ksize} \times \texttt{ksize}$$` separable kernel is used to * calculate the derivative. When `$$\texttt{ksize = 1}$$`, the `$$3 \times 1$$` or `$$1 \times 3$$` * kernel is used (that is, no Gaussian smoothing is done). `ksize = 1` can only be used for the first * or the second x- or y- derivatives. * * There is also the special value `ksize = #FILTER_SCHARR (-1)` that corresponds to the `$$3\times3$$` Scharr * filter that may give more accurate results than the `$$3\times3$$` Sobel. The Scharr aperture is * * `$$\newcommand{\vecthreethree}[9]{ \begin{bmatrix} #1 & #2 & #3\\\\ #4 & #5 & #6\\\\ #7 & #8 & #9 \end{bmatrix} } \vecthreethree{-3}{0}{3}{-10}{0}{10}{-3}{0}{3}$$` * * for the x-derivative, or transposed for the y-derivative. * * The function calculates an image derivative by convolving the image with the appropriate kernel: * * `$$\texttt{dst} = \frac{\partial^{xorder+yorder} \texttt{src}}{\partial x^{xorder} \partial y^{yorder}}$$` * * The Sobel operators combine Gaussian smoothing and differentiation, so the result is more or less * resistant to the noise. Most often, the function is called with ( xorder = 1, yorder = 0, ksize = 3) * or ( xorder = 0, yorder = 1, ksize = 3) to calculate the first x- or y- image derivative. The first * case corresponds to a kernel of: * * `$$\newcommand{\vecthreethree}[9]{ \begin{bmatrix} #1 & #2 & #3\\\\ #4 & #5 & #6\\\\ #7 & #8 & #9 \end{bmatrix} } \vecthreethree{-1}{0}{1}{-2}{0}{2}{-1}{0}{1}$$` * * The second case corresponds to a kernel of: * * `$$\newcommand{\vecthreethree}[9]{ \begin{bmatrix} #1 & #2 & #3\\\\ #4 & #5 & #6\\\\ #7 & #8 & #9 \end{bmatrix} } \vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}$$` * * @param src input image. * @param dst output image of the same size and the same number of channels as src . * @param ddepth output image depth, see REF: filter_depths "combinations"; in the case of * 8-bit input images it will result in truncated derivatives. * @param dx order of the derivative x. * @param dy order of the derivative y. * @param ksize size of the extended Sobel kernel; it must be 1, 3, 5, or 7. * @param scale optional scale factor for the computed derivative values; by default, no scaling is * applied (see #getDerivKernels for details). * @param delta optional delta value that is added to the results prior to storing them in dst. * @param borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported. * @see `+Scharr:dst:ddepth:dx:dy:scale:delta:borderType:`, `+Laplacian:dst:ddepth:ksize:scale:delta:borderType:`, `+sepFilter2D:dst:ddepth:kernelX:kernelY:anchor:delta:borderType:`, `+filter2D:dst:ddepth:kernel:anchor:delta:borderType:`, `+GaussianBlur:dst:ksize:sigmaX:sigmaY:borderType:`, `cartToPolar` */ + (void)Sobel:(Mat*)src dst:(Mat*)dst ddepth:(int)ddepth dx:(int)dx dy:(int)dy ksize:(int)ksize scale:(double)scale delta:(double)delta borderType:(BorderTypes)borderType NS_SWIFT_NAME(Sobel(src:dst:ddepth:dx:dy:ksize:scale:delta:borderType:)); /** * Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator. * * In all cases except one, the `$$\texttt{ksize} \times \texttt{ksize}$$` separable kernel is used to * calculate the derivative. When `$$\texttt{ksize = 1}$$`, the `$$3 \times 1$$` or `$$1 \times 3$$` * kernel is used (that is, no Gaussian smoothing is done). `ksize = 1` can only be used for the first * or the second x- or y- derivatives. * * There is also the special value `ksize = #FILTER_SCHARR (-1)` that corresponds to the `$$3\times3$$` Scharr * filter that may give more accurate results than the `$$3\times3$$` Sobel. The Scharr aperture is * * `$$\newcommand{\vecthreethree}[9]{ \begin{bmatrix} #1 & #2 & #3\\\\ #4 & #5 & #6\\\\ #7 & #8 & #9 \end{bmatrix} } \vecthreethree{-3}{0}{3}{-10}{0}{10}{-3}{0}{3}$$` * * for the x-derivative, or transposed for the y-derivative. * * The function calculates an image derivative by convolving the image with the appropriate kernel: * * `$$\texttt{dst} = \frac{\partial^{xorder+yorder} \texttt{src}}{\partial x^{xorder} \partial y^{yorder}}$$` * * The Sobel operators combine Gaussian smoothing and differentiation, so the result is more or less * resistant to the noise. Most often, the function is called with ( xorder = 1, yorder = 0, ksize = 3) * or ( xorder = 0, yorder = 1, ksize = 3) to calculate the first x- or y- image derivative. The first * case corresponds to a kernel of: * * `$$\newcommand{\vecthreethree}[9]{ \begin{bmatrix} #1 & #2 & #3\\\\ #4 & #5 & #6\\\\ #7 & #8 & #9 \end{bmatrix} } \vecthreethree{-1}{0}{1}{-2}{0}{2}{-1}{0}{1}$$` * * The second case corresponds to a kernel of: * * `$$\newcommand{\vecthreethree}[9]{ \begin{bmatrix} #1 & #2 & #3\\\\ #4 & #5 & #6\\\\ #7 & #8 & #9 \end{bmatrix} } \vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}$$` * * @param src input image. * @param dst output image of the same size and the same number of channels as src . * @param ddepth output image depth, see REF: filter_depths "combinations"; in the case of * 8-bit input images it will result in truncated derivatives. * @param dx order of the derivative x. * @param dy order of the derivative y. * @param ksize size of the extended Sobel kernel; it must be 1, 3, 5, or 7. * @param scale optional scale factor for the computed derivative values; by default, no scaling is * applied (see #getDerivKernels for details). * @param delta optional delta value that is added to the results prior to storing them in dst. * @see `+Scharr:dst:ddepth:dx:dy:scale:delta:borderType:`, `+Laplacian:dst:ddepth:ksize:scale:delta:borderType:`, `+sepFilter2D:dst:ddepth:kernelX:kernelY:anchor:delta:borderType:`, `+filter2D:dst:ddepth:kernel:anchor:delta:borderType:`, `+GaussianBlur:dst:ksize:sigmaX:sigmaY:borderType:`, `cartToPolar` */ + (void)Sobel:(Mat*)src dst:(Mat*)dst ddepth:(int)ddepth dx:(int)dx dy:(int)dy ksize:(int)ksize scale:(double)scale delta:(double)delta NS_SWIFT_NAME(Sobel(src:dst:ddepth:dx:dy:ksize:scale:delta:)); /** * Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator. * * In all cases except one, the `$$\texttt{ksize} \times \texttt{ksize}$$` separable kernel is used to * calculate the derivative. When `$$\texttt{ksize = 1}$$`, the `$$3 \times 1$$` or `$$1 \times 3$$` * kernel is used (that is, no Gaussian smoothing is done). `ksize = 1` can only be used for the first * or the second x- or y- derivatives. * * There is also the special value `ksize = #FILTER_SCHARR (-1)` that corresponds to the `$$3\times3$$` Scharr * filter that may give more accurate results than the `$$3\times3$$` Sobel. The Scharr aperture is * * `$$\newcommand{\vecthreethree}[9]{ \begin{bmatrix} #1 & #2 & #3\\\\ #4 & #5 & #6\\\\ #7 & #8 & #9 \end{bmatrix} } \vecthreethree{-3}{0}{3}{-10}{0}{10}{-3}{0}{3}$$` * * for the x-derivative, or transposed for the y-derivative. * * The function calculates an image derivative by convolving the image with the appropriate kernel: * * `$$\texttt{dst} = \frac{\partial^{xorder+yorder} \texttt{src}}{\partial x^{xorder} \partial y^{yorder}}$$` * * The Sobel operators combine Gaussian smoothing and differentiation, so the result is more or less * resistant to the noise. Most often, the function is called with ( xorder = 1, yorder = 0, ksize = 3) * or ( xorder = 0, yorder = 1, ksize = 3) to calculate the first x- or y- image derivative. The first * case corresponds to a kernel of: * * `$$\newcommand{\vecthreethree}[9]{ \begin{bmatrix} #1 & #2 & #3\\\\ #4 & #5 & #6\\\\ #7 & #8 & #9 \end{bmatrix} } \vecthreethree{-1}{0}{1}{-2}{0}{2}{-1}{0}{1}$$` * * The second case corresponds to a kernel of: * * `$$\newcommand{\vecthreethree}[9]{ \begin{bmatrix} #1 & #2 & #3\\\\ #4 & #5 & #6\\\\ #7 & #8 & #9 \end{bmatrix} } \vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}$$` * * @param src input image. * @param dst output image of the same size and the same number of channels as src . * @param ddepth output image depth, see REF: filter_depths "combinations"; in the case of * 8-bit input images it will result in truncated derivatives. * @param dx order of the derivative x. * @param dy order of the derivative y. * @param ksize size of the extended Sobel kernel; it must be 1, 3, 5, or 7. * @param scale optional scale factor for the computed derivative values; by default, no scaling is * applied (see #getDerivKernels for details). * @see `+Scharr:dst:ddepth:dx:dy:scale:delta:borderType:`, `+Laplacian:dst:ddepth:ksize:scale:delta:borderType:`, `+sepFilter2D:dst:ddepth:kernelX:kernelY:anchor:delta:borderType:`, `+filter2D:dst:ddepth:kernel:anchor:delta:borderType:`, `+GaussianBlur:dst:ksize:sigmaX:sigmaY:borderType:`, `cartToPolar` */ + (void)Sobel:(Mat*)src dst:(Mat*)dst ddepth:(int)ddepth dx:(int)dx dy:(int)dy ksize:(int)ksize scale:(double)scale NS_SWIFT_NAME(Sobel(src:dst:ddepth:dx:dy:ksize:scale:)); /** * Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator. * * In all cases except one, the `$$\texttt{ksize} \times \texttt{ksize}$$` separable kernel is used to * calculate the derivative. When `$$\texttt{ksize = 1}$$`, the `$$3 \times 1$$` or `$$1 \times 3$$` * kernel is used (that is, no Gaussian smoothing is done). `ksize = 1` can only be used for the first * or the second x- or y- derivatives. * * There is also the special value `ksize = #FILTER_SCHARR (-1)` that corresponds to the `$$3\times3$$` Scharr * filter that may give more accurate results than the `$$3\times3$$` Sobel. The Scharr aperture is * * `$$\newcommand{\vecthreethree}[9]{ \begin{bmatrix} #1 & #2 & #3\\\\ #4 & #5 & #6\\\\ #7 & #8 & #9 \end{bmatrix} } \vecthreethree{-3}{0}{3}{-10}{0}{10}{-3}{0}{3}$$` * * for the x-derivative, or transposed for the y-derivative. * * The function calculates an image derivative by convolving the image with the appropriate kernel: * * `$$\texttt{dst} = \frac{\partial^{xorder+yorder} \texttt{src}}{\partial x^{xorder} \partial y^{yorder}}$$` * * The Sobel operators combine Gaussian smoothing and differentiation, so the result is more or less * resistant to the noise. Most often, the function is called with ( xorder = 1, yorder = 0, ksize = 3) * or ( xorder = 0, yorder = 1, ksize = 3) to calculate the first x- or y- image derivative. The first * case corresponds to a kernel of: * * `$$\newcommand{\vecthreethree}[9]{ \begin{bmatrix} #1 & #2 & #3\\\\ #4 & #5 & #6\\\\ #7 & #8 & #9 \end{bmatrix} } \vecthreethree{-1}{0}{1}{-2}{0}{2}{-1}{0}{1}$$` * * The second case corresponds to a kernel of: * * `$$\newcommand{\vecthreethree}[9]{ \begin{bmatrix} #1 & #2 & #3\\\\ #4 & #5 & #6\\\\ #7 & #8 & #9 \end{bmatrix} } \vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}$$` * * @param src input image. * @param dst output image of the same size and the same number of channels as src . * @param ddepth output image depth, see REF: filter_depths "combinations"; in the case of * 8-bit input images it will result in truncated derivatives. * @param dx order of the derivative x. * @param dy order of the derivative y. * @param ksize size of the extended Sobel kernel; it must be 1, 3, 5, or 7. * applied (see #getDerivKernels for details). * @see `+Scharr:dst:ddepth:dx:dy:scale:delta:borderType:`, `+Laplacian:dst:ddepth:ksize:scale:delta:borderType:`, `+sepFilter2D:dst:ddepth:kernelX:kernelY:anchor:delta:borderType:`, `+filter2D:dst:ddepth:kernel:anchor:delta:borderType:`, `+GaussianBlur:dst:ksize:sigmaX:sigmaY:borderType:`, `cartToPolar` */ + (void)Sobel:(Mat*)src dst:(Mat*)dst ddepth:(int)ddepth dx:(int)dx dy:(int)dy ksize:(int)ksize NS_SWIFT_NAME(Sobel(src:dst:ddepth:dx:dy:ksize:)); /** * Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator. * * In all cases except one, the `$$\texttt{ksize} \times \texttt{ksize}$$` separable kernel is used to * calculate the derivative. When `$$\texttt{ksize = 1}$$`, the `$$3 \times 1$$` or `$$1 \times 3$$` * kernel is used (that is, no Gaussian smoothing is done). `ksize = 1` can only be used for the first * or the second x- or y- derivatives. * * There is also the special value `ksize = #FILTER_SCHARR (-1)` that corresponds to the `$$3\times3$$` Scharr * filter that may give more accurate results than the `$$3\times3$$` Sobel. The Scharr aperture is * * `$$\newcommand{\vecthreethree}[9]{ \begin{bmatrix} #1 & #2 & #3\\\\ #4 & #5 & #6\\\\ #7 & #8 & #9 \end{bmatrix} } \vecthreethree{-3}{0}{3}{-10}{0}{10}{-3}{0}{3}$$` * * for the x-derivative, or transposed for the y-derivative. * * The function calculates an image derivative by convolving the image with the appropriate kernel: * * `$$\texttt{dst} = \frac{\partial^{xorder+yorder} \texttt{src}}{\partial x^{xorder} \partial y^{yorder}}$$` * * The Sobel operators combine Gaussian smoothing and differentiation, so the result is more or less * resistant to the noise. Most often, the function is called with ( xorder = 1, yorder = 0, ksize = 3) * or ( xorder = 0, yorder = 1, ksize = 3) to calculate the first x- or y- image derivative. The first * case corresponds to a kernel of: * * `$$\newcommand{\vecthreethree}[9]{ \begin{bmatrix} #1 & #2 & #3\\\\ #4 & #5 & #6\\\\ #7 & #8 & #9 \end{bmatrix} } \vecthreethree{-1}{0}{1}{-2}{0}{2}{-1}{0}{1}$$` * * The second case corresponds to a kernel of: * * `$$\newcommand{\vecthreethree}[9]{ \begin{bmatrix} #1 & #2 & #3\\\\ #4 & #5 & #6\\\\ #7 & #8 & #9 \end{bmatrix} } \vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}$$` * * @param src input image. * @param dst output image of the same size and the same number of channels as src . * @param ddepth output image depth, see REF: filter_depths "combinations"; in the case of * 8-bit input images it will result in truncated derivatives. * @param dx order of the derivative x. * @param dy order of the derivative y. * applied (see #getDerivKernels for details). * @see `+Scharr:dst:ddepth:dx:dy:scale:delta:borderType:`, `+Laplacian:dst:ddepth:ksize:scale:delta:borderType:`, `+sepFilter2D:dst:ddepth:kernelX:kernelY:anchor:delta:borderType:`, `+filter2D:dst:ddepth:kernel:anchor:delta:borderType:`, `+GaussianBlur:dst:ksize:sigmaX:sigmaY:borderType:`, `cartToPolar` */ + (void)Sobel:(Mat*)src dst:(Mat*)dst ddepth:(int)ddepth dx:(int)dx dy:(int)dy NS_SWIFT_NAME(Sobel(src:dst:ddepth:dx:dy:)); // // void cv::spatialGradient(Mat src, Mat& dx, Mat& dy, int ksize = 3, BorderTypes borderType = BORDER_DEFAULT) // /** * Calculates the first order image derivative in both x and y using a Sobel operator * * Equivalent to calling: * * * Sobel( src, dx, CV_16SC1, 1, 0, 3 ); * Sobel( src, dy, CV_16SC1, 0, 1, 3 ); * * * @param src input image. * @param dx output image with first-order derivative in x. * @param dy output image with first-order derivative in y. * @param ksize size of Sobel kernel. It must be 3. * @param borderType pixel extrapolation method, see #BorderTypes. * Only #BORDER_DEFAULT=#BORDER_REFLECT_101 and #BORDER_REPLICATE are supported. * * @see `+Sobel:dst:ddepth:dx:dy:ksize:scale:delta:borderType:` */ + (void)spatialGradient:(Mat*)src dx:(Mat*)dx dy:(Mat*)dy ksize:(int)ksize borderType:(BorderTypes)borderType NS_SWIFT_NAME(spatialGradient(src:dx:dy:ksize:borderType:)); /** * Calculates the first order image derivative in both x and y using a Sobel operator * * Equivalent to calling: * * * Sobel( src, dx, CV_16SC1, 1, 0, 3 ); * Sobel( src, dy, CV_16SC1, 0, 1, 3 ); * * * @param src input image. * @param dx output image with first-order derivative in x. * @param dy output image with first-order derivative in y. * @param ksize size of Sobel kernel. It must be 3. * Only #BORDER_DEFAULT=#BORDER_REFLECT_101 and #BORDER_REPLICATE are supported. * * @see `+Sobel:dst:ddepth:dx:dy:ksize:scale:delta:borderType:` */ + (void)spatialGradient:(Mat*)src dx:(Mat*)dx dy:(Mat*)dy ksize:(int)ksize NS_SWIFT_NAME(spatialGradient(src:dx:dy:ksize:)); /** * Calculates the first order image derivative in both x and y using a Sobel operator * * Equivalent to calling: * * * Sobel( src, dx, CV_16SC1, 1, 0, 3 ); * Sobel( src, dy, CV_16SC1, 0, 1, 3 ); * * * @param src input image. * @param dx output image with first-order derivative in x. * @param dy output image with first-order derivative in y. * Only #BORDER_DEFAULT=#BORDER_REFLECT_101 and #BORDER_REPLICATE are supported. * * @see `+Sobel:dst:ddepth:dx:dy:ksize:scale:delta:borderType:` */ + (void)spatialGradient:(Mat*)src dx:(Mat*)dx dy:(Mat*)dy NS_SWIFT_NAME(spatialGradient(src:dx:dy:)); // // void cv::Scharr(Mat src, Mat& dst, int ddepth, int dx, int dy, double scale = 1, double delta = 0, BorderTypes borderType = BORDER_DEFAULT) // /** * Calculates the first x- or y- image derivative using Scharr operator. * * The function computes the first x- or y- spatial image derivative using the Scharr operator. The * call * * `$$\texttt{Scharr(src, dst, ddepth, dx, dy, scale, delta, borderType)}$$` * * is equivalent to * * `$$\texttt{Sobel(src, dst, ddepth, dx, dy, FILTER\_SCHARR, scale, delta, borderType)} .$$` * * @param src input image. * @param dst output image of the same size and the same number of channels as src. * @param ddepth output image depth, see REF: filter_depths "combinations" * @param dx order of the derivative x. * @param dy order of the derivative y. * @param scale optional scale factor for the computed derivative values; by default, no scaling is * applied (see #getDerivKernels for details). * @param delta optional delta value that is added to the results prior to storing them in dst. * @param borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported. * @see `cartToPolar` */ + (void)Scharr:(Mat*)src dst:(Mat*)dst ddepth:(int)ddepth dx:(int)dx dy:(int)dy scale:(double)scale delta:(double)delta borderType:(BorderTypes)borderType NS_SWIFT_NAME(Scharr(src:dst:ddepth:dx:dy:scale:delta:borderType:)); /** * Calculates the first x- or y- image derivative using Scharr operator. * * The function computes the first x- or y- spatial image derivative using the Scharr operator. The * call * * `$$\texttt{Scharr(src, dst, ddepth, dx, dy, scale, delta, borderType)}$$` * * is equivalent to * * `$$\texttt{Sobel(src, dst, ddepth, dx, dy, FILTER\_SCHARR, scale, delta, borderType)} .$$` * * @param src input image. * @param dst output image of the same size and the same number of channels as src. * @param ddepth output image depth, see REF: filter_depths "combinations" * @param dx order of the derivative x. * @param dy order of the derivative y. * @param scale optional scale factor for the computed derivative values; by default, no scaling is * applied (see #getDerivKernels for details). * @param delta optional delta value that is added to the results prior to storing them in dst. * @see `cartToPolar` */ + (void)Scharr:(Mat*)src dst:(Mat*)dst ddepth:(int)ddepth dx:(int)dx dy:(int)dy scale:(double)scale delta:(double)delta NS_SWIFT_NAME(Scharr(src:dst:ddepth:dx:dy:scale:delta:)); /** * Calculates the first x- or y- image derivative using Scharr operator. * * The function computes the first x- or y- spatial image derivative using the Scharr operator. The * call * * `$$\texttt{Scharr(src, dst, ddepth, dx, dy, scale, delta, borderType)}$$` * * is equivalent to * * `$$\texttt{Sobel(src, dst, ddepth, dx, dy, FILTER\_SCHARR, scale, delta, borderType)} .$$` * * @param src input image. * @param dst output image of the same size and the same number of channels as src. * @param ddepth output image depth, see REF: filter_depths "combinations" * @param dx order of the derivative x. * @param dy order of the derivative y. * @param scale optional scale factor for the computed derivative values; by default, no scaling is * applied (see #getDerivKernels for details). * @see `cartToPolar` */ + (void)Scharr:(Mat*)src dst:(Mat*)dst ddepth:(int)ddepth dx:(int)dx dy:(int)dy scale:(double)scale NS_SWIFT_NAME(Scharr(src:dst:ddepth:dx:dy:scale:)); /** * Calculates the first x- or y- image derivative using Scharr operator. * * The function computes the first x- or y- spatial image derivative using the Scharr operator. The * call * * `$$\texttt{Scharr(src, dst, ddepth, dx, dy, scale, delta, borderType)}$$` * * is equivalent to * * `$$\texttt{Sobel(src, dst, ddepth, dx, dy, FILTER\_SCHARR, scale, delta, borderType)} .$$` * * @param src input image. * @param dst output image of the same size and the same number of channels as src. * @param ddepth output image depth, see REF: filter_depths "combinations" * @param dx order of the derivative x. * @param dy order of the derivative y. * applied (see #getDerivKernels for details). * @see `cartToPolar` */ + (void)Scharr:(Mat*)src dst:(Mat*)dst ddepth:(int)ddepth dx:(int)dx dy:(int)dy NS_SWIFT_NAME(Scharr(src:dst:ddepth:dx:dy:)); // // void cv::Laplacian(Mat src, Mat& dst, int ddepth, int ksize = 1, double scale = 1, double delta = 0, BorderTypes borderType = BORDER_DEFAULT) // /** * Calculates the Laplacian of an image. * * The function calculates the Laplacian of the source image by adding up the second x and y * derivatives calculated using the Sobel operator: * * `$$\texttt{dst} = \Delta \texttt{src} = \frac{\partial^2 \texttt{src}}{\partial x^2} + \frac{\partial^2 \texttt{src}}{\partial y^2}$$` * * This is done when `ksize > 1`. When `ksize == 1`, the Laplacian is computed by filtering the image * with the following `$$3 \times 3$$` aperture: * * `$$\newcommand{\vecthreethree}[9]{ \begin{bmatrix} #1 & #2 & #3\\\\ #4 & #5 & #6\\\\ #7 & #8 & #9 \end{bmatrix} } \vecthreethree {0}{1}{0}{1}{-4}{1}{0}{1}{0}$$` * * @param src Source image. * @param dst Destination image of the same size and the same number of channels as src . * @param ddepth Desired depth of the destination image. * @param ksize Aperture size used to compute the second-derivative filters. See #getDerivKernels for * details. The size must be positive and odd. * @param scale Optional scale factor for the computed Laplacian values. By default, no scaling is * applied. See #getDerivKernels for details. * @param delta Optional delta value that is added to the results prior to storing them in dst . * @param borderType Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported. * @see `+Sobel:dst:ddepth:dx:dy:ksize:scale:delta:borderType:`, `+Scharr:dst:ddepth:dx:dy:scale:delta:borderType:` */ + (void)Laplacian:(Mat*)src dst:(Mat*)dst ddepth:(int)ddepth ksize:(int)ksize scale:(double)scale delta:(double)delta borderType:(BorderTypes)borderType NS_SWIFT_NAME(Laplacian(src:dst:ddepth:ksize:scale:delta:borderType:)); /** * Calculates the Laplacian of an image. * * The function calculates the Laplacian of the source image by adding up the second x and y * derivatives calculated using the Sobel operator: * * `$$\texttt{dst} = \Delta \texttt{src} = \frac{\partial^2 \texttt{src}}{\partial x^2} + \frac{\partial^2 \texttt{src}}{\partial y^2}$$` * * This is done when `ksize > 1`. When `ksize == 1`, the Laplacian is computed by filtering the image * with the following `$$3 \times 3$$` aperture: * * `$$\newcommand{\vecthreethree}[9]{ \begin{bmatrix} #1 & #2 & #3\\\\ #4 & #5 & #6\\\\ #7 & #8 & #9 \end{bmatrix} } \vecthreethree {0}{1}{0}{1}{-4}{1}{0}{1}{0}$$` * * @param src Source image. * @param dst Destination image of the same size and the same number of channels as src . * @param ddepth Desired depth of the destination image. * @param ksize Aperture size used to compute the second-derivative filters. See #getDerivKernels for * details. The size must be positive and odd. * @param scale Optional scale factor for the computed Laplacian values. By default, no scaling is * applied. See #getDerivKernels for details. * @param delta Optional delta value that is added to the results prior to storing them in dst . * @see `+Sobel:dst:ddepth:dx:dy:ksize:scale:delta:borderType:`, `+Scharr:dst:ddepth:dx:dy:scale:delta:borderType:` */ + (void)Laplacian:(Mat*)src dst:(Mat*)dst ddepth:(int)ddepth ksize:(int)ksize scale:(double)scale delta:(double)delta NS_SWIFT_NAME(Laplacian(src:dst:ddepth:ksize:scale:delta:)); /** * Calculates the Laplacian of an image. * * The function calculates the Laplacian of the source image by adding up the second x and y * derivatives calculated using the Sobel operator: * * `$$\texttt{dst} = \Delta \texttt{src} = \frac{\partial^2 \texttt{src}}{\partial x^2} + \frac{\partial^2 \texttt{src}}{\partial y^2}$$` * * This is done when `ksize > 1`. When `ksize == 1`, the Laplacian is computed by filtering the image * with the following `$$3 \times 3$$` aperture: * * `$$\newcommand{\vecthreethree}[9]{ \begin{bmatrix} #1 & #2 & #3\\\\ #4 & #5 & #6\\\\ #7 & #8 & #9 \end{bmatrix} } \vecthreethree {0}{1}{0}{1}{-4}{1}{0}{1}{0}$$` * * @param src Source image. * @param dst Destination image of the same size and the same number of channels as src . * @param ddepth Desired depth of the destination image. * @param ksize Aperture size used to compute the second-derivative filters. See #getDerivKernels for * details. The size must be positive and odd. * @param scale Optional scale factor for the computed Laplacian values. By default, no scaling is * applied. See #getDerivKernels for details. * @see `+Sobel:dst:ddepth:dx:dy:ksize:scale:delta:borderType:`, `+Scharr:dst:ddepth:dx:dy:scale:delta:borderType:` */ + (void)Laplacian:(Mat*)src dst:(Mat*)dst ddepth:(int)ddepth ksize:(int)ksize scale:(double)scale NS_SWIFT_NAME(Laplacian(src:dst:ddepth:ksize:scale:)); /** * Calculates the Laplacian of an image. * * The function calculates the Laplacian of the source image by adding up the second x and y * derivatives calculated using the Sobel operator: * * `$$\texttt{dst} = \Delta \texttt{src} = \frac{\partial^2 \texttt{src}}{\partial x^2} + \frac{\partial^2 \texttt{src}}{\partial y^2}$$` * * This is done when `ksize > 1`. When `ksize == 1`, the Laplacian is computed by filtering the image * with the following `$$3 \times 3$$` aperture: * * `$$\newcommand{\vecthreethree}[9]{ \begin{bmatrix} #1 & #2 & #3\\\\ #4 & #5 & #6\\\\ #7 & #8 & #9 \end{bmatrix} } \vecthreethree {0}{1}{0}{1}{-4}{1}{0}{1}{0}$$` * * @param src Source image. * @param dst Destination image of the same size and the same number of channels as src . * @param ddepth Desired depth of the destination image. * @param ksize Aperture size used to compute the second-derivative filters. See #getDerivKernels for * details. The size must be positive and odd. * applied. See #getDerivKernels for details. * @see `+Sobel:dst:ddepth:dx:dy:ksize:scale:delta:borderType:`, `+Scharr:dst:ddepth:dx:dy:scale:delta:borderType:` */ + (void)Laplacian:(Mat*)src dst:(Mat*)dst ddepth:(int)ddepth ksize:(int)ksize NS_SWIFT_NAME(Laplacian(src:dst:ddepth:ksize:)); /** * Calculates the Laplacian of an image. * * The function calculates the Laplacian of the source image by adding up the second x and y * derivatives calculated using the Sobel operator: * * `$$\texttt{dst} = \Delta \texttt{src} = \frac{\partial^2 \texttt{src}}{\partial x^2} + \frac{\partial^2 \texttt{src}}{\partial y^2}$$` * * This is done when `ksize > 1`. When `ksize == 1`, the Laplacian is computed by filtering the image * with the following `$$3 \times 3$$` aperture: * * `$$\newcommand{\vecthreethree}[9]{ \begin{bmatrix} #1 & #2 & #3\\\\ #4 & #5 & #6\\\\ #7 & #8 & #9 \end{bmatrix} } \vecthreethree {0}{1}{0}{1}{-4}{1}{0}{1}{0}$$` * * @param src Source image. * @param dst Destination image of the same size and the same number of channels as src . * @param ddepth Desired depth of the destination image. * details. The size must be positive and odd. * applied. See #getDerivKernels for details. * @see `+Sobel:dst:ddepth:dx:dy:ksize:scale:delta:borderType:`, `+Scharr:dst:ddepth:dx:dy:scale:delta:borderType:` */ + (void)Laplacian:(Mat*)src dst:(Mat*)dst ddepth:(int)ddepth NS_SWIFT_NAME(Laplacian(src:dst:ddepth:)); // // void cv::Canny(Mat image, Mat& edges, double threshold1, double threshold2, int apertureSize = 3, bool L2gradient = false) // /** * Finds edges in an image using the Canny algorithm CITE: Canny86 . * * The function finds edges in the input image and marks them in the output map edges using the * Canny algorithm. The smallest value between threshold1 and threshold2 is used for edge linking. The * largest value is used to find initial segments of strong edges. See * * * @param image 8-bit input image. * @param edges output edge map; single channels 8-bit image, which has the same size as image . * @param threshold1 first threshold for the hysteresis procedure. * @param threshold2 second threshold for the hysteresis procedure. * @param apertureSize aperture size for the Sobel operator. * @param L2gradient a flag, indicating whether a more accurate `$$L_2$$` norm * `$$=\sqrt{(dI/dx)^2 + (dI/dy)^2}$$` should be used to calculate the image gradient magnitude ( * L2gradient=true ), or whether the default `$$L_1$$` norm `$$=|dI/dx|+|dI/dy|$$` is enough ( * L2gradient=false ). */ + (void)Canny:(Mat*)image edges:(Mat*)edges threshold1:(double)threshold1 threshold2:(double)threshold2 apertureSize:(int)apertureSize L2gradient:(BOOL)L2gradient NS_SWIFT_NAME(Canny(image:edges:threshold1:threshold2:apertureSize:L2gradient:)); /** * Finds edges in an image using the Canny algorithm CITE: Canny86 . * * The function finds edges in the input image and marks them in the output map edges using the * Canny algorithm. The smallest value between threshold1 and threshold2 is used for edge linking. The * largest value is used to find initial segments of strong edges. See * * * @param image 8-bit input image. * @param edges output edge map; single channels 8-bit image, which has the same size as image . * @param threshold1 first threshold for the hysteresis procedure. * @param threshold2 second threshold for the hysteresis procedure. * @param apertureSize aperture size for the Sobel operator. * `$$=\sqrt{(dI/dx)^2 + (dI/dy)^2}$$` should be used to calculate the image gradient magnitude ( * L2gradient=true ), or whether the default `$$L_1$$` norm `$$=|dI/dx|+|dI/dy|$$` is enough ( * L2gradient=false ). */ + (void)Canny:(Mat*)image edges:(Mat*)edges threshold1:(double)threshold1 threshold2:(double)threshold2 apertureSize:(int)apertureSize NS_SWIFT_NAME(Canny(image:edges:threshold1:threshold2:apertureSize:)); /** * Finds edges in an image using the Canny algorithm CITE: Canny86 . * * The function finds edges in the input image and marks them in the output map edges using the * Canny algorithm. The smallest value between threshold1 and threshold2 is used for edge linking. The * largest value is used to find initial segments of strong edges. See * * * @param image 8-bit input image. * @param edges output edge map; single channels 8-bit image, which has the same size as image . * @param threshold1 first threshold for the hysteresis procedure. * @param threshold2 second threshold for the hysteresis procedure. * `$$=\sqrt{(dI/dx)^2 + (dI/dy)^2}$$` should be used to calculate the image gradient magnitude ( * L2gradient=true ), or whether the default `$$L_1$$` norm `$$=|dI/dx|+|dI/dy|$$` is enough ( * L2gradient=false ). */ + (void)Canny:(Mat*)image edges:(Mat*)edges threshold1:(double)threshold1 threshold2:(double)threshold2 NS_SWIFT_NAME(Canny(image:edges:threshold1:threshold2:)); // // void cv::Canny(Mat dx, Mat dy, Mat& edges, double threshold1, double threshold2, bool L2gradient = false) // /** * \overload * * Finds edges in an image using the Canny algorithm with custom image gradient. * * @param dx 16-bit x derivative of input image (CV_16SC1 or CV_16SC3). * @param dy 16-bit y derivative of input image (same type as dx). * @param edges output edge map; single channels 8-bit image, which has the same size as image . * @param threshold1 first threshold for the hysteresis procedure. * @param threshold2 second threshold for the hysteresis procedure. * @param L2gradient a flag, indicating whether a more accurate `$$L_2$$` norm * `$$=\sqrt{(dI/dx)^2 + (dI/dy)^2}$$` should be used to calculate the image gradient magnitude ( * L2gradient=true ), or whether the default `$$L_1$$` norm `$$=|dI/dx|+|dI/dy|$$` is enough ( * L2gradient=false ). */ + (void)Canny:(Mat*)dx dy:(Mat*)dy edges:(Mat*)edges threshold1:(double)threshold1 threshold2:(double)threshold2 L2gradient:(BOOL)L2gradient NS_SWIFT_NAME(Canny(dx:dy:edges:threshold1:threshold2:L2gradient:)); /** * \overload * * Finds edges in an image using the Canny algorithm with custom image gradient. * * @param dx 16-bit x derivative of input image (CV_16SC1 or CV_16SC3). * @param dy 16-bit y derivative of input image (same type as dx). * @param edges output edge map; single channels 8-bit image, which has the same size as image . * @param threshold1 first threshold for the hysteresis procedure. * @param threshold2 second threshold for the hysteresis procedure. * `$$=\sqrt{(dI/dx)^2 + (dI/dy)^2}$$` should be used to calculate the image gradient magnitude ( * L2gradient=true ), or whether the default `$$L_1$$` norm `$$=|dI/dx|+|dI/dy|$$` is enough ( * L2gradient=false ). */ + (void)Canny:(Mat*)dx dy:(Mat*)dy edges:(Mat*)edges threshold1:(double)threshold1 threshold2:(double)threshold2 NS_SWIFT_NAME(Canny(dx:dy:edges:threshold1:threshold2:)); // // void cv::cornerMinEigenVal(Mat src, Mat& dst, int blockSize, int ksize = 3, BorderTypes borderType = BORDER_DEFAULT) // /** * Calculates the minimal eigenvalue of gradient matrices for corner detection. * * The function is similar to cornerEigenValsAndVecs but it calculates and stores only the minimal * eigenvalue of the covariance matrix of derivatives, that is, `$$\min(\lambda_1, \lambda_2)$$` in terms * of the formulae in the cornerEigenValsAndVecs description. * * @param src Input single-channel 8-bit or floating-point image. * @param dst Image to store the minimal eigenvalues. It has the type CV_32FC1 and the same size as * src . * @param blockSize Neighborhood size (see the details on #cornerEigenValsAndVecs ). * @param ksize Aperture parameter for the Sobel operator. * @param borderType Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported. */ + (void)cornerMinEigenVal:(Mat*)src dst:(Mat*)dst blockSize:(int)blockSize ksize:(int)ksize borderType:(BorderTypes)borderType NS_SWIFT_NAME(cornerMinEigenVal(src:dst:blockSize:ksize:borderType:)); /** * Calculates the minimal eigenvalue of gradient matrices for corner detection. * * The function is similar to cornerEigenValsAndVecs but it calculates and stores only the minimal * eigenvalue of the covariance matrix of derivatives, that is, `$$\min(\lambda_1, \lambda_2)$$` in terms * of the formulae in the cornerEigenValsAndVecs description. * * @param src Input single-channel 8-bit or floating-point image. * @param dst Image to store the minimal eigenvalues. It has the type CV_32FC1 and the same size as * src . * @param blockSize Neighborhood size (see the details on #cornerEigenValsAndVecs ). * @param ksize Aperture parameter for the Sobel operator. */ + (void)cornerMinEigenVal:(Mat*)src dst:(Mat*)dst blockSize:(int)blockSize ksize:(int)ksize NS_SWIFT_NAME(cornerMinEigenVal(src:dst:blockSize:ksize:)); /** * Calculates the minimal eigenvalue of gradient matrices for corner detection. * * The function is similar to cornerEigenValsAndVecs but it calculates and stores only the minimal * eigenvalue of the covariance matrix of derivatives, that is, `$$\min(\lambda_1, \lambda_2)$$` in terms * of the formulae in the cornerEigenValsAndVecs description. * * @param src Input single-channel 8-bit or floating-point image. * @param dst Image to store the minimal eigenvalues. It has the type CV_32FC1 and the same size as * src . * @param blockSize Neighborhood size (see the details on #cornerEigenValsAndVecs ). */ + (void)cornerMinEigenVal:(Mat*)src dst:(Mat*)dst blockSize:(int)blockSize NS_SWIFT_NAME(cornerMinEigenVal(src:dst:blockSize:)); // // void cv::cornerHarris(Mat src, Mat& dst, int blockSize, int ksize, double k, BorderTypes borderType = BORDER_DEFAULT) // /** * Harris corner detector. * * The function runs the Harris corner detector on the image. Similarly to cornerMinEigenVal and * cornerEigenValsAndVecs , for each pixel `$$(x, y)$$` it calculates a `$$2\times2$$` gradient covariance * matrix `$$M^{(x,y)}$$` over a `$$\texttt{blockSize} \times \texttt{blockSize}$$` neighborhood. Then, it * computes the following characteristic: * * `$$\texttt{dst} (x,y) = \mathrm{det} M^{(x,y)} - k \cdot \left ( \mathrm{tr} M^{(x,y)} \right )^2$$` * * Corners in the image can be found as the local maxima of this response map. * * @param src Input single-channel 8-bit or floating-point image. * @param dst Image to store the Harris detector responses. It has the type CV_32FC1 and the same * size as src . * @param blockSize Neighborhood size (see the details on #cornerEigenValsAndVecs ). * @param ksize Aperture parameter for the Sobel operator. * @param k Harris detector free parameter. See the formula above. * @param borderType Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported. */ + (void)cornerHarris:(Mat*)src dst:(Mat*)dst blockSize:(int)blockSize ksize:(int)ksize k:(double)k borderType:(BorderTypes)borderType NS_SWIFT_NAME(cornerHarris(src:dst:blockSize:ksize:k:borderType:)); /** * Harris corner detector. * * The function runs the Harris corner detector on the image. Similarly to cornerMinEigenVal and * cornerEigenValsAndVecs , for each pixel `$$(x, y)$$` it calculates a `$$2\times2$$` gradient covariance * matrix `$$M^{(x,y)}$$` over a `$$\texttt{blockSize} \times \texttt{blockSize}$$` neighborhood. Then, it * computes the following characteristic: * * `$$\texttt{dst} (x,y) = \mathrm{det} M^{(x,y)} - k \cdot \left ( \mathrm{tr} M^{(x,y)} \right )^2$$` * * Corners in the image can be found as the local maxima of this response map. * * @param src Input single-channel 8-bit or floating-point image. * @param dst Image to store the Harris detector responses. It has the type CV_32FC1 and the same * size as src . * @param blockSize Neighborhood size (see the details on #cornerEigenValsAndVecs ). * @param ksize Aperture parameter for the Sobel operator. * @param k Harris detector free parameter. See the formula above. */ + (void)cornerHarris:(Mat*)src dst:(Mat*)dst blockSize:(int)blockSize ksize:(int)ksize k:(double)k NS_SWIFT_NAME(cornerHarris(src:dst:blockSize:ksize:k:)); // // void cv::cornerEigenValsAndVecs(Mat src, Mat& dst, int blockSize, int ksize, BorderTypes borderType = BORDER_DEFAULT) // /** * Calculates eigenvalues and eigenvectors of image blocks for corner detection. * * For every pixel `$$p$$` , the function cornerEigenValsAndVecs considers a blockSize `$$\times$$` blockSize * neighborhood `$$S(p)$$` . It calculates the covariation matrix of derivatives over the neighborhood as: * * `$$M = \begin{bmatrix} \sum _{S(p)}(dI/dx)^2 & \sum _{S(p)}dI/dx dI/dy \\ \sum _{S(p)}dI/dx dI/dy & \sum _{S(p)}(dI/dy)^2 \end{bmatrix}$$` * * where the derivatives are computed using the Sobel operator. * * After that, it finds eigenvectors and eigenvalues of `$$M$$` and stores them in the destination image as * `$$(\lambda_1, \lambda_2, x_1, y_1, x_2, y_2)$$` where * * - `$$\lambda_1, \lambda_2$$` are the non-sorted eigenvalues of `$$M$$` * - `$$x_1, y_1$$` are the eigenvectors corresponding to `$$\lambda_1$$` * - `$$x_2, y_2$$` are the eigenvectors corresponding to `$$\lambda_2$$` * * The output of the function can be used for robust edge or corner detection. * * @param src Input single-channel 8-bit or floating-point image. * @param dst Image to store the results. It has the same size as src and the type CV_32FC(6) . * @param blockSize Neighborhood size (see details below). * @param ksize Aperture parameter for the Sobel operator. * @param borderType Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported. * * @see `+cornerMinEigenVal:dst:blockSize:ksize:borderType:`, `+cornerHarris:dst:blockSize:ksize:k:borderType:`, `+preCornerDetect:dst:ksize:borderType:` */ + (void)cornerEigenValsAndVecs:(Mat*)src dst:(Mat*)dst blockSize:(int)blockSize ksize:(int)ksize borderType:(BorderTypes)borderType NS_SWIFT_NAME(cornerEigenValsAndVecs(src:dst:blockSize:ksize:borderType:)); /** * Calculates eigenvalues and eigenvectors of image blocks for corner detection. * * For every pixel `$$p$$` , the function cornerEigenValsAndVecs considers a blockSize `$$\times$$` blockSize * neighborhood `$$S(p)$$` . It calculates the covariation matrix of derivatives over the neighborhood as: * * `$$M = \begin{bmatrix} \sum _{S(p)}(dI/dx)^2 & \sum _{S(p)}dI/dx dI/dy \\ \sum _{S(p)}dI/dx dI/dy & \sum _{S(p)}(dI/dy)^2 \end{bmatrix}$$` * * where the derivatives are computed using the Sobel operator. * * After that, it finds eigenvectors and eigenvalues of `$$M$$` and stores them in the destination image as * `$$(\lambda_1, \lambda_2, x_1, y_1, x_2, y_2)$$` where * * - `$$\lambda_1, \lambda_2$$` are the non-sorted eigenvalues of `$$M$$` * - `$$x_1, y_1$$` are the eigenvectors corresponding to `$$\lambda_1$$` * - `$$x_2, y_2$$` are the eigenvectors corresponding to `$$\lambda_2$$` * * The output of the function can be used for robust edge or corner detection. * * @param src Input single-channel 8-bit or floating-point image. * @param dst Image to store the results. It has the same size as src and the type CV_32FC(6) . * @param blockSize Neighborhood size (see details below). * @param ksize Aperture parameter for the Sobel operator. * * @see `+cornerMinEigenVal:dst:blockSize:ksize:borderType:`, `+cornerHarris:dst:blockSize:ksize:k:borderType:`, `+preCornerDetect:dst:ksize:borderType:` */ + (void)cornerEigenValsAndVecs:(Mat*)src dst:(Mat*)dst blockSize:(int)blockSize ksize:(int)ksize NS_SWIFT_NAME(cornerEigenValsAndVecs(src:dst:blockSize:ksize:)); // // void cv::preCornerDetect(Mat src, Mat& dst, int ksize, BorderTypes borderType = BORDER_DEFAULT) // /** * Calculates a feature map for corner detection. * * The function calculates the complex spatial derivative-based function of the source image * * `$$\texttt{dst} = (D_x \texttt{src} )^2 \cdot D_{yy} \texttt{src} + (D_y \texttt{src} )^2 \cdot D_{xx} \texttt{src} - 2 D_x \texttt{src} \cdot D_y \texttt{src} \cdot D_{xy} \texttt{src}$$` * * where `$$D_x$$`,`$$D_y$$` are the first image derivatives, `$$D_{xx}$$`,`$$D_{yy}$$` are the second image * derivatives, and `$$D_{xy}$$` is the mixed derivative. * * The corners can be found as local maximums of the functions, as shown below: * * Mat corners, dilated_corners; * preCornerDetect(image, corners, 3); * // dilation with 3x3 rectangular structuring element * dilate(corners, dilated_corners, Mat(), 1); * Mat corner_mask = corners == dilated_corners; * * * @param src Source single-channel 8-bit of floating-point image. * @param dst Output image that has the type CV_32F and the same size as src . * @param ksize %Aperture size of the Sobel . * @param borderType Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported. */ + (void)preCornerDetect:(Mat*)src dst:(Mat*)dst ksize:(int)ksize borderType:(BorderTypes)borderType NS_SWIFT_NAME(preCornerDetect(src:dst:ksize:borderType:)); /** * Calculates a feature map for corner detection. * * The function calculates the complex spatial derivative-based function of the source image * * `$$\texttt{dst} = (D_x \texttt{src} )^2 \cdot D_{yy} \texttt{src} + (D_y \texttt{src} )^2 \cdot D_{xx} \texttt{src} - 2 D_x \texttt{src} \cdot D_y \texttt{src} \cdot D_{xy} \texttt{src}$$` * * where `$$D_x$$`,`$$D_y$$` are the first image derivatives, `$$D_{xx}$$`,`$$D_{yy}$$` are the second image * derivatives, and `$$D_{xy}$$` is the mixed derivative. * * The corners can be found as local maximums of the functions, as shown below: * * Mat corners, dilated_corners; * preCornerDetect(image, corners, 3); * // dilation with 3x3 rectangular structuring element * dilate(corners, dilated_corners, Mat(), 1); * Mat corner_mask = corners == dilated_corners; * * * @param src Source single-channel 8-bit of floating-point image. * @param dst Output image that has the type CV_32F and the same size as src . * @param ksize %Aperture size of the Sobel . */ + (void)preCornerDetect:(Mat*)src dst:(Mat*)dst ksize:(int)ksize NS_SWIFT_NAME(preCornerDetect(src:dst:ksize:)); // // void cv::cornerSubPix(Mat image, Mat& corners, Size winSize, Size zeroZone, TermCriteria criteria) // /** * Refines the corner locations. * * The function iterates to find the sub-pixel accurate location of corners or radial saddle * points as described in CITE: forstner1987fast, and as shown on the figure below. * * ![image](pics/cornersubpix.png) * * Sub-pixel accurate corner locator is based on the observation that every vector from the center `$$q$$` * to a point `$$p$$` located within a neighborhood of `$$q$$` is orthogonal to the image gradient at `$$p$$` * subject to image and measurement noise. Consider the expression: * * `$$\epsilon _i = {DI_{p_i}}^T \cdot (q - p_i)$$` * * where `$${DI_{p_i}}$$` is an image gradient at one of the points `$$p_i$$` in a neighborhood of `$$q$$` . The * value of `$$q$$` is to be found so that `$$\epsilon_i$$` is minimized. A system of equations may be set up * with `$$\epsilon_i$$` set to zero: * * `$$\sum _i(DI_{p_i} \cdot {DI_{p_i}}^T) \cdot q - \sum _i(DI_{p_i} \cdot {DI_{p_i}}^T \cdot p_i)$$` * * where the gradients are summed within a neighborhood ("search window") of `$$q$$` . Calling the first * gradient term `$$G$$` and the second gradient term `$$b$$` gives: * * `$$q = G^{-1} \cdot b$$` * * The algorithm sets the center of the neighborhood window at this new center `$$q$$` and then iterates * until the center stays within a set threshold. * * @param image Input single-channel, 8-bit or float image. * @param corners Initial coordinates of the input corners and refined coordinates provided for * output. * @param winSize Half of the side length of the search window. For example, if winSize=Size(5,5) , * then a `$$(5*2+1) \times (5*2+1) = 11 \times 11$$` search window is used. * @param zeroZone Half of the size of the dead region in the middle of the search zone over which * the summation in the formula below is not done. It is used sometimes to avoid possible * singularities of the autocorrelation matrix. The value of (-1,-1) indicates that there is no such * a size. * @param criteria Criteria for termination of the iterative process of corner refinement. That is, * the process of corner position refinement stops either after criteria.maxCount iterations or when * the corner position moves by less than criteria.epsilon on some iteration. */ + (void)cornerSubPix:(Mat*)image corners:(Mat*)corners winSize:(Size2i*)winSize zeroZone:(Size2i*)zeroZone criteria:(TermCriteria*)criteria NS_SWIFT_NAME(cornerSubPix(image:corners:winSize:zeroZone:criteria:)); // // void cv::goodFeaturesToTrack(Mat image, vector_Point& corners, int maxCorners, double qualityLevel, double minDistance, Mat mask = Mat(), int blockSize = 3, bool useHarrisDetector = false, double k = 0.04) // /** * Determines strong corners on an image. * * The function finds the most prominent corners in the image or in the specified image region, as * described in CITE: Shi94 * * - Function calculates the corner quality measure at every source image pixel using the * #cornerMinEigenVal or #cornerHarris . * - Function performs a non-maximum suppression (the local maximums in *3 x 3* neighborhood are * retained). * - The corners with the minimal eigenvalue less than * `$$\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)$$` are rejected. * - The remaining corners are sorted by the quality measure in the descending order. * - Function throws away each corner for which there is a stronger corner at a distance less than * maxDistance. * * The function can be used to initialize a point-based tracker of an object. * * NOTE: If the function is called with different values A and B of the parameter qualityLevel , and * A \> B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector * with qualityLevel=B . * * @param image Input 8-bit or floating-point 32-bit, single-channel image. * @param corners Output vector of detected corners. * @param maxCorners Maximum number of corners to return. If there are more corners than are found, * the strongest of them is returned. `maxCorners <= 0` implies that no limit on the maximum is set * and all detected corners are returned. * @param qualityLevel Parameter characterizing the minimal accepted quality of image corners. The * parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue * (see #cornerMinEigenVal ) or the Harris function response (see #cornerHarris ). The corners with the * quality measure less than the product are rejected. For example, if the best corner has the * quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure * less than 15 are rejected. * @param minDistance Minimum possible Euclidean distance between the returned corners. * @param mask Optional region of interest. If the image is not empty (it needs to have the type * CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected. * @param blockSize Size of an average block for computing a derivative covariation matrix over each * pixel neighborhood. See cornerEigenValsAndVecs . * @param useHarrisDetector Parameter indicating whether to use a Harris detector (see #cornerHarris) * or #cornerMinEigenVal. * @param k Free parameter of the Harris detector. * * @see `+cornerMinEigenVal:dst:blockSize:ksize:borderType:`, `+cornerHarris:dst:blockSize:ksize:k:borderType:`, `calcOpticalFlowPyrLK`, `estimateRigidTransform`, `` */ + (void)goodFeaturesToTrack:(Mat*)image corners:(NSMutableArray*)corners maxCorners:(int)maxCorners qualityLevel:(double)qualityLevel minDistance:(double)minDistance mask:(Mat*)mask blockSize:(int)blockSize useHarrisDetector:(BOOL)useHarrisDetector k:(double)k NS_SWIFT_NAME(goodFeaturesToTrack(image:corners:maxCorners:qualityLevel:minDistance:mask:blockSize:useHarrisDetector:k:)); /** * Determines strong corners on an image. * * The function finds the most prominent corners in the image or in the specified image region, as * described in CITE: Shi94 * * - Function calculates the corner quality measure at every source image pixel using the * #cornerMinEigenVal or #cornerHarris . * - Function performs a non-maximum suppression (the local maximums in *3 x 3* neighborhood are * retained). * - The corners with the minimal eigenvalue less than * `$$\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)$$` are rejected. * - The remaining corners are sorted by the quality measure in the descending order. * - Function throws away each corner for which there is a stronger corner at a distance less than * maxDistance. * * The function can be used to initialize a point-based tracker of an object. * * NOTE: If the function is called with different values A and B of the parameter qualityLevel , and * A \> B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector * with qualityLevel=B . * * @param image Input 8-bit or floating-point 32-bit, single-channel image. * @param corners Output vector of detected corners. * @param maxCorners Maximum number of corners to return. If there are more corners than are found, * the strongest of them is returned. `maxCorners <= 0` implies that no limit on the maximum is set * and all detected corners are returned. * @param qualityLevel Parameter characterizing the minimal accepted quality of image corners. The * parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue * (see #cornerMinEigenVal ) or the Harris function response (see #cornerHarris ). The corners with the * quality measure less than the product are rejected. For example, if the best corner has the * quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure * less than 15 are rejected. * @param minDistance Minimum possible Euclidean distance between the returned corners. * @param mask Optional region of interest. If the image is not empty (it needs to have the type * CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected. * @param blockSize Size of an average block for computing a derivative covariation matrix over each * pixel neighborhood. See cornerEigenValsAndVecs . * @param useHarrisDetector Parameter indicating whether to use a Harris detector (see #cornerHarris) * or #cornerMinEigenVal. * * @see `+cornerMinEigenVal:dst:blockSize:ksize:borderType:`, `+cornerHarris:dst:blockSize:ksize:k:borderType:`, `calcOpticalFlowPyrLK`, `estimateRigidTransform`, `` */ + (void)goodFeaturesToTrack:(Mat*)image corners:(NSMutableArray*)corners maxCorners:(int)maxCorners qualityLevel:(double)qualityLevel minDistance:(double)minDistance mask:(Mat*)mask blockSize:(int)blockSize useHarrisDetector:(BOOL)useHarrisDetector NS_SWIFT_NAME(goodFeaturesToTrack(image:corners:maxCorners:qualityLevel:minDistance:mask:blockSize:useHarrisDetector:)); /** * Determines strong corners on an image. * * The function finds the most prominent corners in the image or in the specified image region, as * described in CITE: Shi94 * * - Function calculates the corner quality measure at every source image pixel using the * #cornerMinEigenVal or #cornerHarris . * - Function performs a non-maximum suppression (the local maximums in *3 x 3* neighborhood are * retained). * - The corners with the minimal eigenvalue less than * `$$\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)$$` are rejected. * - The remaining corners are sorted by the quality measure in the descending order. * - Function throws away each corner for which there is a stronger corner at a distance less than * maxDistance. * * The function can be used to initialize a point-based tracker of an object. * * NOTE: If the function is called with different values A and B of the parameter qualityLevel , and * A \> B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector * with qualityLevel=B . * * @param image Input 8-bit or floating-point 32-bit, single-channel image. * @param corners Output vector of detected corners. * @param maxCorners Maximum number of corners to return. If there are more corners than are found, * the strongest of them is returned. `maxCorners <= 0` implies that no limit on the maximum is set * and all detected corners are returned. * @param qualityLevel Parameter characterizing the minimal accepted quality of image corners. The * parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue * (see #cornerMinEigenVal ) or the Harris function response (see #cornerHarris ). The corners with the * quality measure less than the product are rejected. For example, if the best corner has the * quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure * less than 15 are rejected. * @param minDistance Minimum possible Euclidean distance between the returned corners. * @param mask Optional region of interest. If the image is not empty (it needs to have the type * CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected. * @param blockSize Size of an average block for computing a derivative covariation matrix over each * pixel neighborhood. See cornerEigenValsAndVecs . * or #cornerMinEigenVal. * * @see `+cornerMinEigenVal:dst:blockSize:ksize:borderType:`, `+cornerHarris:dst:blockSize:ksize:k:borderType:`, `calcOpticalFlowPyrLK`, `estimateRigidTransform`, `` */ + (void)goodFeaturesToTrack:(Mat*)image corners:(NSMutableArray*)corners maxCorners:(int)maxCorners qualityLevel:(double)qualityLevel minDistance:(double)minDistance mask:(Mat*)mask blockSize:(int)blockSize NS_SWIFT_NAME(goodFeaturesToTrack(image:corners:maxCorners:qualityLevel:minDistance:mask:blockSize:)); /** * Determines strong corners on an image. * * The function finds the most prominent corners in the image or in the specified image region, as * described in CITE: Shi94 * * - Function calculates the corner quality measure at every source image pixel using the * #cornerMinEigenVal or #cornerHarris . * - Function performs a non-maximum suppression (the local maximums in *3 x 3* neighborhood are * retained). * - The corners with the minimal eigenvalue less than * `$$\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)$$` are rejected. * - The remaining corners are sorted by the quality measure in the descending order. * - Function throws away each corner for which there is a stronger corner at a distance less than * maxDistance. * * The function can be used to initialize a point-based tracker of an object. * * NOTE: If the function is called with different values A and B of the parameter qualityLevel , and * A \> B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector * with qualityLevel=B . * * @param image Input 8-bit or floating-point 32-bit, single-channel image. * @param corners Output vector of detected corners. * @param maxCorners Maximum number of corners to return. If there are more corners than are found, * the strongest of them is returned. `maxCorners <= 0` implies that no limit on the maximum is set * and all detected corners are returned. * @param qualityLevel Parameter characterizing the minimal accepted quality of image corners. The * parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue * (see #cornerMinEigenVal ) or the Harris function response (see #cornerHarris ). The corners with the * quality measure less than the product are rejected. For example, if the best corner has the * quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure * less than 15 are rejected. * @param minDistance Minimum possible Euclidean distance between the returned corners. * @param mask Optional region of interest. If the image is not empty (it needs to have the type * CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected. * pixel neighborhood. See cornerEigenValsAndVecs . * or #cornerMinEigenVal. * * @see `+cornerMinEigenVal:dst:blockSize:ksize:borderType:`, `+cornerHarris:dst:blockSize:ksize:k:borderType:`, `calcOpticalFlowPyrLK`, `estimateRigidTransform`, `` */ + (void)goodFeaturesToTrack:(Mat*)image corners:(NSMutableArray*)corners maxCorners:(int)maxCorners qualityLevel:(double)qualityLevel minDistance:(double)minDistance mask:(Mat*)mask NS_SWIFT_NAME(goodFeaturesToTrack(image:corners:maxCorners:qualityLevel:minDistance:mask:)); /** * Determines strong corners on an image. * * The function finds the most prominent corners in the image or in the specified image region, as * described in CITE: Shi94 * * - Function calculates the corner quality measure at every source image pixel using the * #cornerMinEigenVal or #cornerHarris . * - Function performs a non-maximum suppression (the local maximums in *3 x 3* neighborhood are * retained). * - The corners with the minimal eigenvalue less than * `$$\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)$$` are rejected. * - The remaining corners are sorted by the quality measure in the descending order. * - Function throws away each corner for which there is a stronger corner at a distance less than * maxDistance. * * The function can be used to initialize a point-based tracker of an object. * * NOTE: If the function is called with different values A and B of the parameter qualityLevel , and * A \> B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector * with qualityLevel=B . * * @param image Input 8-bit or floating-point 32-bit, single-channel image. * @param corners Output vector of detected corners. * @param maxCorners Maximum number of corners to return. If there are more corners than are found, * the strongest of them is returned. `maxCorners <= 0` implies that no limit on the maximum is set * and all detected corners are returned. * @param qualityLevel Parameter characterizing the minimal accepted quality of image corners. The * parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue * (see #cornerMinEigenVal ) or the Harris function response (see #cornerHarris ). The corners with the * quality measure less than the product are rejected. For example, if the best corner has the * quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure * less than 15 are rejected. * @param minDistance Minimum possible Euclidean distance between the returned corners. * CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected. * pixel neighborhood. See cornerEigenValsAndVecs . * or #cornerMinEigenVal. * * @see `+cornerMinEigenVal:dst:blockSize:ksize:borderType:`, `+cornerHarris:dst:blockSize:ksize:k:borderType:`, `calcOpticalFlowPyrLK`, `estimateRigidTransform`, `` */ + (void)goodFeaturesToTrack:(Mat*)image corners:(NSMutableArray*)corners maxCorners:(int)maxCorners qualityLevel:(double)qualityLevel minDistance:(double)minDistance NS_SWIFT_NAME(goodFeaturesToTrack(image:corners:maxCorners:qualityLevel:minDistance:)); // // void cv::goodFeaturesToTrack(Mat image, vector_Point& corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize, int gradientSize, bool useHarrisDetector = false, double k = 0.04) // + (void)goodFeaturesToTrack:(Mat*)image corners:(NSMutableArray*)corners maxCorners:(int)maxCorners qualityLevel:(double)qualityLevel minDistance:(double)minDistance mask:(Mat*)mask blockSize:(int)blockSize gradientSize:(int)gradientSize useHarrisDetector:(BOOL)useHarrisDetector k:(double)k NS_SWIFT_NAME(goodFeaturesToTrack(image:corners:maxCorners:qualityLevel:minDistance:mask:blockSize:gradientSize:useHarrisDetector:k:)); + (void)goodFeaturesToTrack:(Mat*)image corners:(NSMutableArray*)corners maxCorners:(int)maxCorners qualityLevel:(double)qualityLevel minDistance:(double)minDistance mask:(Mat*)mask blockSize:(int)blockSize gradientSize:(int)gradientSize useHarrisDetector:(BOOL)useHarrisDetector NS_SWIFT_NAME(goodFeaturesToTrack(image:corners:maxCorners:qualityLevel:minDistance:mask:blockSize:gradientSize:useHarrisDetector:)); + (void)goodFeaturesToTrack:(Mat*)image corners:(NSMutableArray*)corners maxCorners:(int)maxCorners qualityLevel:(double)qualityLevel minDistance:(double)minDistance mask:(Mat*)mask blockSize:(int)blockSize gradientSize:(int)gradientSize NS_SWIFT_NAME(goodFeaturesToTrack(image:corners:maxCorners:qualityLevel:minDistance:mask:blockSize:gradientSize:)); // // void cv::goodFeaturesToTrack(Mat image, Mat& corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, Mat& cornersQuality, int blockSize = 3, int gradientSize = 3, bool useHarrisDetector = false, double k = 0.04) // /** * Same as above, but returns also quality measure of the detected corners. * * @param image Input 8-bit or floating-point 32-bit, single-channel image. * @param corners Output vector of detected corners. * @param maxCorners Maximum number of corners to return. If there are more corners than are found, * the strongest of them is returned. `maxCorners <= 0` implies that no limit on the maximum is set * and all detected corners are returned. * @param qualityLevel Parameter characterizing the minimal accepted quality of image corners. The * parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue * (see #cornerMinEigenVal ) or the Harris function response (see #cornerHarris ). The corners with the * quality measure less than the product are rejected. For example, if the best corner has the * quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure * less than 15 are rejected. * @param minDistance Minimum possible Euclidean distance between the returned corners. * @param mask Region of interest. If the image is not empty (it needs to have the type * CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected. * @param cornersQuality Output vector of quality measure of the detected corners. * @param blockSize Size of an average block for computing a derivative covariation matrix over each * pixel neighborhood. See cornerEigenValsAndVecs . * @param gradientSize Aperture parameter for the Sobel operator used for derivatives computation. * See cornerEigenValsAndVecs . * @param useHarrisDetector Parameter indicating whether to use a Harris detector (see #cornerHarris) * or #cornerMinEigenVal. * @param k Free parameter of the Harris detector. */ + (void)goodFeaturesToTrackWithQuality:(Mat*)image corners:(Mat*)corners maxCorners:(int)maxCorners qualityLevel:(double)qualityLevel minDistance:(double)minDistance mask:(Mat*)mask cornersQuality:(Mat*)cornersQuality blockSize:(int)blockSize gradientSize:(int)gradientSize useHarrisDetector:(BOOL)useHarrisDetector k:(double)k NS_SWIFT_NAME(goodFeaturesToTrack(image:corners:maxCorners:qualityLevel:minDistance:mask:cornersQuality:blockSize:gradientSize:useHarrisDetector:k:)); /** * Same as above, but returns also quality measure of the detected corners. * * @param image Input 8-bit or floating-point 32-bit, single-channel image. * @param corners Output vector of detected corners. * @param maxCorners Maximum number of corners to return. If there are more corners than are found, * the strongest of them is returned. `maxCorners <= 0` implies that no limit on the maximum is set * and all detected corners are returned. * @param qualityLevel Parameter characterizing the minimal accepted quality of image corners. The * parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue * (see #cornerMinEigenVal ) or the Harris function response (see #cornerHarris ). The corners with the * quality measure less than the product are rejected. For example, if the best corner has the * quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure * less than 15 are rejected. * @param minDistance Minimum possible Euclidean distance between the returned corners. * @param mask Region of interest. If the image is not empty (it needs to have the type * CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected. * @param cornersQuality Output vector of quality measure of the detected corners. * @param blockSize Size of an average block for computing a derivative covariation matrix over each * pixel neighborhood. See cornerEigenValsAndVecs . * @param gradientSize Aperture parameter for the Sobel operator used for derivatives computation. * See cornerEigenValsAndVecs . * @param useHarrisDetector Parameter indicating whether to use a Harris detector (see #cornerHarris) * or #cornerMinEigenVal. */ + (void)goodFeaturesToTrackWithQuality:(Mat*)image corners:(Mat*)corners maxCorners:(int)maxCorners qualityLevel:(double)qualityLevel minDistance:(double)minDistance mask:(Mat*)mask cornersQuality:(Mat*)cornersQuality blockSize:(int)blockSize gradientSize:(int)gradientSize useHarrisDetector:(BOOL)useHarrisDetector NS_SWIFT_NAME(goodFeaturesToTrack(image:corners:maxCorners:qualityLevel:minDistance:mask:cornersQuality:blockSize:gradientSize:useHarrisDetector:)); /** * Same as above, but returns also quality measure of the detected corners. * * @param image Input 8-bit or floating-point 32-bit, single-channel image. * @param corners Output vector of detected corners. * @param maxCorners Maximum number of corners to return. If there are more corners than are found, * the strongest of them is returned. `maxCorners <= 0` implies that no limit on the maximum is set * and all detected corners are returned. * @param qualityLevel Parameter characterizing the minimal accepted quality of image corners. The * parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue * (see #cornerMinEigenVal ) or the Harris function response (see #cornerHarris ). The corners with the * quality measure less than the product are rejected. For example, if the best corner has the * quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure * less than 15 are rejected. * @param minDistance Minimum possible Euclidean distance between the returned corners. * @param mask Region of interest. If the image is not empty (it needs to have the type * CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected. * @param cornersQuality Output vector of quality measure of the detected corners. * @param blockSize Size of an average block for computing a derivative covariation matrix over each * pixel neighborhood. See cornerEigenValsAndVecs . * @param gradientSize Aperture parameter for the Sobel operator used for derivatives computation. * See cornerEigenValsAndVecs . * or #cornerMinEigenVal. */ + (void)goodFeaturesToTrackWithQuality:(Mat*)image corners:(Mat*)corners maxCorners:(int)maxCorners qualityLevel:(double)qualityLevel minDistance:(double)minDistance mask:(Mat*)mask cornersQuality:(Mat*)cornersQuality blockSize:(int)blockSize gradientSize:(int)gradientSize NS_SWIFT_NAME(goodFeaturesToTrack(image:corners:maxCorners:qualityLevel:minDistance:mask:cornersQuality:blockSize:gradientSize:)); /** * Same as above, but returns also quality measure of the detected corners. * * @param image Input 8-bit or floating-point 32-bit, single-channel image. * @param corners Output vector of detected corners. * @param maxCorners Maximum number of corners to return. If there are more corners than are found, * the strongest of them is returned. `maxCorners <= 0` implies that no limit on the maximum is set * and all detected corners are returned. * @param qualityLevel Parameter characterizing the minimal accepted quality of image corners. The * parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue * (see #cornerMinEigenVal ) or the Harris function response (see #cornerHarris ). The corners with the * quality measure less than the product are rejected. For example, if the best corner has the * quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure * less than 15 are rejected. * @param minDistance Minimum possible Euclidean distance between the returned corners. * @param mask Region of interest. If the image is not empty (it needs to have the type * CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected. * @param cornersQuality Output vector of quality measure of the detected corners. * @param blockSize Size of an average block for computing a derivative covariation matrix over each * pixel neighborhood. See cornerEigenValsAndVecs . * See cornerEigenValsAndVecs . * or #cornerMinEigenVal. */ + (void)goodFeaturesToTrackWithQuality:(Mat*)image corners:(Mat*)corners maxCorners:(int)maxCorners qualityLevel:(double)qualityLevel minDistance:(double)minDistance mask:(Mat*)mask cornersQuality:(Mat*)cornersQuality blockSize:(int)blockSize NS_SWIFT_NAME(goodFeaturesToTrack(image:corners:maxCorners:qualityLevel:minDistance:mask:cornersQuality:blockSize:)); /** * Same as above, but returns also quality measure of the detected corners. * * @param image Input 8-bit or floating-point 32-bit, single-channel image. * @param corners Output vector of detected corners. * @param maxCorners Maximum number of corners to return. If there are more corners than are found, * the strongest of them is returned. `maxCorners <= 0` implies that no limit on the maximum is set * and all detected corners are returned. * @param qualityLevel Parameter characterizing the minimal accepted quality of image corners. The * parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue * (see #cornerMinEigenVal ) or the Harris function response (see #cornerHarris ). The corners with the * quality measure less than the product are rejected. For example, if the best corner has the * quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure * less than 15 are rejected. * @param minDistance Minimum possible Euclidean distance between the returned corners. * @param mask Region of interest. If the image is not empty (it needs to have the type * CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected. * @param cornersQuality Output vector of quality measure of the detected corners. * pixel neighborhood. See cornerEigenValsAndVecs . * See cornerEigenValsAndVecs . * or #cornerMinEigenVal. */ + (void)goodFeaturesToTrackWithQuality:(Mat*)image corners:(Mat*)corners maxCorners:(int)maxCorners qualityLevel:(double)qualityLevel minDistance:(double)minDistance mask:(Mat*)mask cornersQuality:(Mat*)cornersQuality NS_SWIFT_NAME(goodFeaturesToTrack(image:corners:maxCorners:qualityLevel:minDistance:mask:cornersQuality:)); // // void cv::HoughLines(Mat image, Mat& lines, double rho, double theta, int threshold, double srn = 0, double stn = 0, double min_theta = 0, double max_theta = CV_PI) // /** * Finds lines in a binary image using the standard Hough transform. * * The function implements the standard or standard multi-scale Hough transform algorithm for line * detection. See for a good explanation of Hough * transform. * * @param image 8-bit, single-channel binary source image. The image may be modified by the function. * @param lines Output vector of lines. Each line is represented by a 2 or 3 element vector * `$$(\rho, \theta)$$` or `$$(\rho, \theta, \textrm{votes})$$` . `$$\rho$$` is the distance from the coordinate origin `$$(0,0)$$` (top-left corner of * the image). `$$\theta$$` is the line rotation angle in radians ( * `$$0 \sim \textrm{vertical line}, \pi/2 \sim \textrm{horizontal line}$$` ). * `$$\textrm{votes}$$` is the value of accumulator. * @param rho Distance resolution of the accumulator in pixels. * @param theta Angle resolution of the accumulator in radians. * @param threshold Accumulator threshold parameter. Only those lines are returned that get enough * votes ( `$$>\texttt{threshold}$$` ). * @param srn For the multi-scale Hough transform, it is a divisor for the distance resolution rho . * The coarse accumulator distance resolution is rho and the accurate accumulator resolution is * rho/srn . If both srn=0 and stn=0 , the classical Hough transform is used. Otherwise, both these * parameters should be positive. * @param stn For the multi-scale Hough transform, it is a divisor for the distance resolution theta. * @param min_theta For standard and multi-scale Hough transform, minimum angle to check for lines. * Must fall between 0 and max_theta. * @param max_theta For standard and multi-scale Hough transform, maximum angle to check for lines. * Must fall between min_theta and CV_PI. */ + (void)HoughLines:(Mat*)image lines:(Mat*)lines rho:(double)rho theta:(double)theta threshold:(int)threshold srn:(double)srn stn:(double)stn min_theta:(double)min_theta max_theta:(double)max_theta NS_SWIFT_NAME(HoughLines(image:lines:rho:theta:threshold:srn:stn:min_theta:max_theta:)); /** * Finds lines in a binary image using the standard Hough transform. * * The function implements the standard or standard multi-scale Hough transform algorithm for line * detection. See for a good explanation of Hough * transform. * * @param image 8-bit, single-channel binary source image. The image may be modified by the function. * @param lines Output vector of lines. Each line is represented by a 2 or 3 element vector * `$$(\rho, \theta)$$` or `$$(\rho, \theta, \textrm{votes})$$` . `$$\rho$$` is the distance from the coordinate origin `$$(0,0)$$` (top-left corner of * the image). `$$\theta$$` is the line rotation angle in radians ( * `$$0 \sim \textrm{vertical line}, \pi/2 \sim \textrm{horizontal line}$$` ). * `$$\textrm{votes}$$` is the value of accumulator. * @param rho Distance resolution of the accumulator in pixels. * @param theta Angle resolution of the accumulator in radians. * @param threshold Accumulator threshold parameter. Only those lines are returned that get enough * votes ( `$$>\texttt{threshold}$$` ). * @param srn For the multi-scale Hough transform, it is a divisor for the distance resolution rho . * The coarse accumulator distance resolution is rho and the accurate accumulator resolution is * rho/srn . If both srn=0 and stn=0 , the classical Hough transform is used. Otherwise, both these * parameters should be positive. * @param stn For the multi-scale Hough transform, it is a divisor for the distance resolution theta. * @param min_theta For standard and multi-scale Hough transform, minimum angle to check for lines. * Must fall between 0 and max_theta. * Must fall between min_theta and CV_PI. */ + (void)HoughLines:(Mat*)image lines:(Mat*)lines rho:(double)rho theta:(double)theta threshold:(int)threshold srn:(double)srn stn:(double)stn min_theta:(double)min_theta NS_SWIFT_NAME(HoughLines(image:lines:rho:theta:threshold:srn:stn:min_theta:)); /** * Finds lines in a binary image using the standard Hough transform. * * The function implements the standard or standard multi-scale Hough transform algorithm for line * detection. See for a good explanation of Hough * transform. * * @param image 8-bit, single-channel binary source image. The image may be modified by the function. * @param lines Output vector of lines. Each line is represented by a 2 or 3 element vector * `$$(\rho, \theta)$$` or `$$(\rho, \theta, \textrm{votes})$$` . `$$\rho$$` is the distance from the coordinate origin `$$(0,0)$$` (top-left corner of * the image). `$$\theta$$` is the line rotation angle in radians ( * `$$0 \sim \textrm{vertical line}, \pi/2 \sim \textrm{horizontal line}$$` ). * `$$\textrm{votes}$$` is the value of accumulator. * @param rho Distance resolution of the accumulator in pixels. * @param theta Angle resolution of the accumulator in radians. * @param threshold Accumulator threshold parameter. Only those lines are returned that get enough * votes ( `$$>\texttt{threshold}$$` ). * @param srn For the multi-scale Hough transform, it is a divisor for the distance resolution rho . * The coarse accumulator distance resolution is rho and the accurate accumulator resolution is * rho/srn . If both srn=0 and stn=0 , the classical Hough transform is used. Otherwise, both these * parameters should be positive. * @param stn For the multi-scale Hough transform, it is a divisor for the distance resolution theta. * Must fall between 0 and max_theta. * Must fall between min_theta and CV_PI. */ + (void)HoughLines:(Mat*)image lines:(Mat*)lines rho:(double)rho theta:(double)theta threshold:(int)threshold srn:(double)srn stn:(double)stn NS_SWIFT_NAME(HoughLines(image:lines:rho:theta:threshold:srn:stn:)); /** * Finds lines in a binary image using the standard Hough transform. * * The function implements the standard or standard multi-scale Hough transform algorithm for line * detection. See for a good explanation of Hough * transform. * * @param image 8-bit, single-channel binary source image. The image may be modified by the function. * @param lines Output vector of lines. Each line is represented by a 2 or 3 element vector * `$$(\rho, \theta)$$` or `$$(\rho, \theta, \textrm{votes})$$` . `$$\rho$$` is the distance from the coordinate origin `$$(0,0)$$` (top-left corner of * the image). `$$\theta$$` is the line rotation angle in radians ( * `$$0 \sim \textrm{vertical line}, \pi/2 \sim \textrm{horizontal line}$$` ). * `$$\textrm{votes}$$` is the value of accumulator. * @param rho Distance resolution of the accumulator in pixels. * @param theta Angle resolution of the accumulator in radians. * @param threshold Accumulator threshold parameter. Only those lines are returned that get enough * votes ( `$$>\texttt{threshold}$$` ). * @param srn For the multi-scale Hough transform, it is a divisor for the distance resolution rho . * The coarse accumulator distance resolution is rho and the accurate accumulator resolution is * rho/srn . If both srn=0 and stn=0 , the classical Hough transform is used. Otherwise, both these * parameters should be positive. * Must fall between 0 and max_theta. * Must fall between min_theta and CV_PI. */ + (void)HoughLines:(Mat*)image lines:(Mat*)lines rho:(double)rho theta:(double)theta threshold:(int)threshold srn:(double)srn NS_SWIFT_NAME(HoughLines(image:lines:rho:theta:threshold:srn:)); /** * Finds lines in a binary image using the standard Hough transform. * * The function implements the standard or standard multi-scale Hough transform algorithm for line * detection. See for a good explanation of Hough * transform. * * @param image 8-bit, single-channel binary source image. The image may be modified by the function. * @param lines Output vector of lines. Each line is represented by a 2 or 3 element vector * `$$(\rho, \theta)$$` or `$$(\rho, \theta, \textrm{votes})$$` . `$$\rho$$` is the distance from the coordinate origin `$$(0,0)$$` (top-left corner of * the image). `$$\theta$$` is the line rotation angle in radians ( * `$$0 \sim \textrm{vertical line}, \pi/2 \sim \textrm{horizontal line}$$` ). * `$$\textrm{votes}$$` is the value of accumulator. * @param rho Distance resolution of the accumulator in pixels. * @param theta Angle resolution of the accumulator in radians. * @param threshold Accumulator threshold parameter. Only those lines are returned that get enough * votes ( `$$>\texttt{threshold}$$` ). * The coarse accumulator distance resolution is rho and the accurate accumulator resolution is * rho/srn . If both srn=0 and stn=0 , the classical Hough transform is used. Otherwise, both these * parameters should be positive. * Must fall between 0 and max_theta. * Must fall between min_theta and CV_PI. */ + (void)HoughLines:(Mat*)image lines:(Mat*)lines rho:(double)rho theta:(double)theta threshold:(int)threshold NS_SWIFT_NAME(HoughLines(image:lines:rho:theta:threshold:)); // // void cv::HoughLinesP(Mat image, Mat& lines, double rho, double theta, int threshold, double minLineLength = 0, double maxLineGap = 0) // /** * Finds line segments in a binary image using the probabilistic Hough transform. * * The function implements the probabilistic Hough transform algorithm for line detection, described * in CITE: Matas00 * * See the line detection example below: * INCLUDE: snippets/imgproc_HoughLinesP.cpp * This is a sample picture the function parameters have been tuned for: * * ![image](pics/building.jpg) * * And this is the output of the above program in case of the probabilistic Hough transform: * * ![image](pics/houghp.png) * * @param image 8-bit, single-channel binary source image. The image may be modified by the function. * @param lines Output vector of lines. Each line is represented by a 4-element vector * `$$(x_1, y_1, x_2, y_2)$$` , where `$$(x_1,y_1)$$` and `$$(x_2, y_2)$$` are the ending points of each detected * line segment. * @param rho Distance resolution of the accumulator in pixels. * @param theta Angle resolution of the accumulator in radians. * @param threshold Accumulator threshold parameter. Only those lines are returned that get enough * votes ( `$$>\texttt{threshold}$$` ). * @param minLineLength Minimum line length. Line segments shorter than that are rejected. * @param maxLineGap Maximum allowed gap between points on the same line to link them. * * @see `LineSegmentDetector` */ + (void)HoughLinesP:(Mat*)image lines:(Mat*)lines rho:(double)rho theta:(double)theta threshold:(int)threshold minLineLength:(double)minLineLength maxLineGap:(double)maxLineGap NS_SWIFT_NAME(HoughLinesP(image:lines:rho:theta:threshold:minLineLength:maxLineGap:)); /** * Finds line segments in a binary image using the probabilistic Hough transform. * * The function implements the probabilistic Hough transform algorithm for line detection, described * in CITE: Matas00 * * See the line detection example below: * INCLUDE: snippets/imgproc_HoughLinesP.cpp * This is a sample picture the function parameters have been tuned for: * * ![image](pics/building.jpg) * * And this is the output of the above program in case of the probabilistic Hough transform: * * ![image](pics/houghp.png) * * @param image 8-bit, single-channel binary source image. The image may be modified by the function. * @param lines Output vector of lines. Each line is represented by a 4-element vector * `$$(x_1, y_1, x_2, y_2)$$` , where `$$(x_1,y_1)$$` and `$$(x_2, y_2)$$` are the ending points of each detected * line segment. * @param rho Distance resolution of the accumulator in pixels. * @param theta Angle resolution of the accumulator in radians. * @param threshold Accumulator threshold parameter. Only those lines are returned that get enough * votes ( `$$>\texttt{threshold}$$` ). * @param minLineLength Minimum line length. Line segments shorter than that are rejected. * * @see `LineSegmentDetector` */ + (void)HoughLinesP:(Mat*)image lines:(Mat*)lines rho:(double)rho theta:(double)theta threshold:(int)threshold minLineLength:(double)minLineLength NS_SWIFT_NAME(HoughLinesP(image:lines:rho:theta:threshold:minLineLength:)); /** * Finds line segments in a binary image using the probabilistic Hough transform. * * The function implements the probabilistic Hough transform algorithm for line detection, described * in CITE: Matas00 * * See the line detection example below: * INCLUDE: snippets/imgproc_HoughLinesP.cpp * This is a sample picture the function parameters have been tuned for: * * ![image](pics/building.jpg) * * And this is the output of the above program in case of the probabilistic Hough transform: * * ![image](pics/houghp.png) * * @param image 8-bit, single-channel binary source image. The image may be modified by the function. * @param lines Output vector of lines. Each line is represented by a 4-element vector * `$$(x_1, y_1, x_2, y_2)$$` , where `$$(x_1,y_1)$$` and `$$(x_2, y_2)$$` are the ending points of each detected * line segment. * @param rho Distance resolution of the accumulator in pixels. * @param theta Angle resolution of the accumulator in radians. * @param threshold Accumulator threshold parameter. Only those lines are returned that get enough * votes ( `$$>\texttt{threshold}$$` ). * * @see `LineSegmentDetector` */ + (void)HoughLinesP:(Mat*)image lines:(Mat*)lines rho:(double)rho theta:(double)theta threshold:(int)threshold NS_SWIFT_NAME(HoughLinesP(image:lines:rho:theta:threshold:)); // // void cv::HoughLinesPointSet(Mat point, Mat& lines, int lines_max, int threshold, double min_rho, double max_rho, double rho_step, double min_theta, double max_theta, double theta_step) // /** * Finds lines in a set of points using the standard Hough transform. * * The function finds lines in a set of points using a modification of the Hough transform. * INCLUDE: snippets/imgproc_HoughLinesPointSet.cpp * @param point Input vector of points. Each vector must be encoded as a Point vector `$$(x,y)$$`. Type must be CV_32FC2 or CV_32SC2. * @param lines Output vector of found lines. Each vector is encoded as a vector `$$(votes, rho, theta)$$`. * The larger the value of 'votes', the higher the reliability of the Hough line. * @param lines_max Max count of Hough lines. * @param threshold Accumulator threshold parameter. Only those lines are returned that get enough * votes ( `$$>\texttt{threshold}$$` ). * @param min_rho Minimum value for `$$\rho$$` for the accumulator (Note: `$$\rho$$` can be negative. The absolute value `$$|\rho|$$` is the distance of a line to the origin.). * @param max_rho Maximum value for `$$\rho$$` for the accumulator. * @param rho_step Distance resolution of the accumulator. * @param min_theta Minimum angle value of the accumulator in radians. * @param max_theta Maximum angle value of the accumulator in radians. * @param theta_step Angle resolution of the accumulator in radians. */ + (void)HoughLinesPointSet:(Mat*)point lines:(Mat*)lines lines_max:(int)lines_max threshold:(int)threshold min_rho:(double)min_rho max_rho:(double)max_rho rho_step:(double)rho_step min_theta:(double)min_theta max_theta:(double)max_theta theta_step:(double)theta_step NS_SWIFT_NAME(HoughLinesPointSet(point:lines:lines_max:threshold:min_rho:max_rho:rho_step:min_theta:max_theta:theta_step:)); // // void cv::HoughCircles(Mat image, Mat& circles, HoughModes method, double dp, double minDist, double param1 = 100, double param2 = 100, int minRadius = 0, int maxRadius = 0) // /** * Finds circles in a grayscale image using the Hough transform. * * The function finds circles in a grayscale image using a modification of the Hough transform. * * Example: : * INCLUDE: snippets/imgproc_HoughLinesCircles.cpp * * NOTE: Usually the function detects the centers of circles well. However, it may fail to find correct * radii. You can assist to the function by specifying the radius range ( minRadius and maxRadius ) if * you know it. Or, in the case of #HOUGH_GRADIENT method you may set maxRadius to a negative number * to return centers only without radius search, and find the correct radius using an additional procedure. * * It also helps to smooth image a bit unless it's already soft. For example, * GaussianBlur() with 7x7 kernel and 1.5x1.5 sigma or similar blurring may help. * * @param image 8-bit, single-channel, grayscale input image. * @param circles Output vector of found circles. Each vector is encoded as 3 or 4 element * floating-point vector `$$(x, y, radius)$$` or `$$(x, y, radius, votes)$$` . * @param method Detection method, see #HoughModes. The available methods are #HOUGH_GRADIENT and #HOUGH_GRADIENT_ALT. * @param dp Inverse ratio of the accumulator resolution to the image resolution. For example, if * dp=1 , the accumulator has the same resolution as the input image. If dp=2 , the accumulator has * half as big width and height. For #HOUGH_GRADIENT_ALT the recommended value is dp=1.5, * unless some small very circles need to be detected. * @param minDist Minimum distance between the centers of the detected circles. If the parameter is * too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is * too large, some circles may be missed. * @param param1 First method-specific parameter. In case of #HOUGH_GRADIENT and #HOUGH_GRADIENT_ALT, * it is the higher threshold of the two passed to the Canny edge detector (the lower one is twice smaller). * Note that #HOUGH_GRADIENT_ALT uses #Scharr algorithm to compute image derivatives, so the threshold value * shough normally be higher, such as 300 or normally exposed and contrasty images. * @param param2 Second method-specific parameter. In case of #HOUGH_GRADIENT, it is the * accumulator threshold for the circle centers at the detection stage. The smaller it is, the more * false circles may be detected. Circles, corresponding to the larger accumulator values, will be * returned first. In the case of #HOUGH_GRADIENT_ALT algorithm, this is the circle "perfectness" measure. * The closer it to 1, the better shaped circles algorithm selects. In most cases 0.9 should be fine. * If you want get better detection of small circles, you may decrease it to 0.85, 0.8 or even less. * But then also try to limit the search range [minRadius, maxRadius] to avoid many false circles. * @param minRadius Minimum circle radius. * @param maxRadius Maximum circle radius. If <= 0, uses the maximum image dimension. If < 0, #HOUGH_GRADIENT returns * centers without finding the radius. #HOUGH_GRADIENT_ALT always computes circle radiuses. * * @see `+fitEllipse:`, `+minEnclosingCircle:center:radius:` */ + (void)HoughCircles:(Mat*)image circles:(Mat*)circles method:(HoughModes)method dp:(double)dp minDist:(double)minDist param1:(double)param1 param2:(double)param2 minRadius:(int)minRadius maxRadius:(int)maxRadius NS_SWIFT_NAME(HoughCircles(image:circles:method:dp:minDist:param1:param2:minRadius:maxRadius:)); /** * Finds circles in a grayscale image using the Hough transform. * * The function finds circles in a grayscale image using a modification of the Hough transform. * * Example: : * INCLUDE: snippets/imgproc_HoughLinesCircles.cpp * * NOTE: Usually the function detects the centers of circles well. However, it may fail to find correct * radii. You can assist to the function by specifying the radius range ( minRadius and maxRadius ) if * you know it. Or, in the case of #HOUGH_GRADIENT method you may set maxRadius to a negative number * to return centers only without radius search, and find the correct radius using an additional procedure. * * It also helps to smooth image a bit unless it's already soft. For example, * GaussianBlur() with 7x7 kernel and 1.5x1.5 sigma or similar blurring may help. * * @param image 8-bit, single-channel, grayscale input image. * @param circles Output vector of found circles. Each vector is encoded as 3 or 4 element * floating-point vector `$$(x, y, radius)$$` or `$$(x, y, radius, votes)$$` . * @param method Detection method, see #HoughModes. The available methods are #HOUGH_GRADIENT and #HOUGH_GRADIENT_ALT. * @param dp Inverse ratio of the accumulator resolution to the image resolution. For example, if * dp=1 , the accumulator has the same resolution as the input image. If dp=2 , the accumulator has * half as big width and height. For #HOUGH_GRADIENT_ALT the recommended value is dp=1.5, * unless some small very circles need to be detected. * @param minDist Minimum distance between the centers of the detected circles. If the parameter is * too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is * too large, some circles may be missed. * @param param1 First method-specific parameter. In case of #HOUGH_GRADIENT and #HOUGH_GRADIENT_ALT, * it is the higher threshold of the two passed to the Canny edge detector (the lower one is twice smaller). * Note that #HOUGH_GRADIENT_ALT uses #Scharr algorithm to compute image derivatives, so the threshold value * shough normally be higher, such as 300 or normally exposed and contrasty images. * @param param2 Second method-specific parameter. In case of #HOUGH_GRADIENT, it is the * accumulator threshold for the circle centers at the detection stage. The smaller it is, the more * false circles may be detected. Circles, corresponding to the larger accumulator values, will be * returned first. In the case of #HOUGH_GRADIENT_ALT algorithm, this is the circle "perfectness" measure. * The closer it to 1, the better shaped circles algorithm selects. In most cases 0.9 should be fine. * If you want get better detection of small circles, you may decrease it to 0.85, 0.8 or even less. * But then also try to limit the search range [minRadius, maxRadius] to avoid many false circles. * @param minRadius Minimum circle radius. * centers without finding the radius. #HOUGH_GRADIENT_ALT always computes circle radiuses. * * @see `+fitEllipse:`, `+minEnclosingCircle:center:radius:` */ + (void)HoughCircles:(Mat*)image circles:(Mat*)circles method:(HoughModes)method dp:(double)dp minDist:(double)minDist param1:(double)param1 param2:(double)param2 minRadius:(int)minRadius NS_SWIFT_NAME(HoughCircles(image:circles:method:dp:minDist:param1:param2:minRadius:)); /** * Finds circles in a grayscale image using the Hough transform. * * The function finds circles in a grayscale image using a modification of the Hough transform. * * Example: : * INCLUDE: snippets/imgproc_HoughLinesCircles.cpp * * NOTE: Usually the function detects the centers of circles well. However, it may fail to find correct * radii. You can assist to the function by specifying the radius range ( minRadius and maxRadius ) if * you know it. Or, in the case of #HOUGH_GRADIENT method you may set maxRadius to a negative number * to return centers only without radius search, and find the correct radius using an additional procedure. * * It also helps to smooth image a bit unless it's already soft. For example, * GaussianBlur() with 7x7 kernel and 1.5x1.5 sigma or similar blurring may help. * * @param image 8-bit, single-channel, grayscale input image. * @param circles Output vector of found circles. Each vector is encoded as 3 or 4 element * floating-point vector `$$(x, y, radius)$$` or `$$(x, y, radius, votes)$$` . * @param method Detection method, see #HoughModes. The available methods are #HOUGH_GRADIENT and #HOUGH_GRADIENT_ALT. * @param dp Inverse ratio of the accumulator resolution to the image resolution. For example, if * dp=1 , the accumulator has the same resolution as the input image. If dp=2 , the accumulator has * half as big width and height. For #HOUGH_GRADIENT_ALT the recommended value is dp=1.5, * unless some small very circles need to be detected. * @param minDist Minimum distance between the centers of the detected circles. If the parameter is * too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is * too large, some circles may be missed. * @param param1 First method-specific parameter. In case of #HOUGH_GRADIENT and #HOUGH_GRADIENT_ALT, * it is the higher threshold of the two passed to the Canny edge detector (the lower one is twice smaller). * Note that #HOUGH_GRADIENT_ALT uses #Scharr algorithm to compute image derivatives, so the threshold value * shough normally be higher, such as 300 or normally exposed and contrasty images. * @param param2 Second method-specific parameter. In case of #HOUGH_GRADIENT, it is the * accumulator threshold for the circle centers at the detection stage. The smaller it is, the more * false circles may be detected. Circles, corresponding to the larger accumulator values, will be * returned first. In the case of #HOUGH_GRADIENT_ALT algorithm, this is the circle "perfectness" measure. * The closer it to 1, the better shaped circles algorithm selects. In most cases 0.9 should be fine. * If you want get better detection of small circles, you may decrease it to 0.85, 0.8 or even less. * But then also try to limit the search range [minRadius, maxRadius] to avoid many false circles. * centers without finding the radius. #HOUGH_GRADIENT_ALT always computes circle radiuses. * * @see `+fitEllipse:`, `+minEnclosingCircle:center:radius:` */ + (void)HoughCircles:(Mat*)image circles:(Mat*)circles method:(HoughModes)method dp:(double)dp minDist:(double)minDist param1:(double)param1 param2:(double)param2 NS_SWIFT_NAME(HoughCircles(image:circles:method:dp:minDist:param1:param2:)); /** * Finds circles in a grayscale image using the Hough transform. * * The function finds circles in a grayscale image using a modification of the Hough transform. * * Example: : * INCLUDE: snippets/imgproc_HoughLinesCircles.cpp * * NOTE: Usually the function detects the centers of circles well. However, it may fail to find correct * radii. You can assist to the function by specifying the radius range ( minRadius and maxRadius ) if * you know it. Or, in the case of #HOUGH_GRADIENT method you may set maxRadius to a negative number * to return centers only without radius search, and find the correct radius using an additional procedure. * * It also helps to smooth image a bit unless it's already soft. For example, * GaussianBlur() with 7x7 kernel and 1.5x1.5 sigma or similar blurring may help. * * @param image 8-bit, single-channel, grayscale input image. * @param circles Output vector of found circles. Each vector is encoded as 3 or 4 element * floating-point vector `$$(x, y, radius)$$` or `$$(x, y, radius, votes)$$` . * @param method Detection method, see #HoughModes. The available methods are #HOUGH_GRADIENT and #HOUGH_GRADIENT_ALT. * @param dp Inverse ratio of the accumulator resolution to the image resolution. For example, if * dp=1 , the accumulator has the same resolution as the input image. If dp=2 , the accumulator has * half as big width and height. For #HOUGH_GRADIENT_ALT the recommended value is dp=1.5, * unless some small very circles need to be detected. * @param minDist Minimum distance between the centers of the detected circles. If the parameter is * too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is * too large, some circles may be missed. * @param param1 First method-specific parameter. In case of #HOUGH_GRADIENT and #HOUGH_GRADIENT_ALT, * it is the higher threshold of the two passed to the Canny edge detector (the lower one is twice smaller). * Note that #HOUGH_GRADIENT_ALT uses #Scharr algorithm to compute image derivatives, so the threshold value * shough normally be higher, such as 300 or normally exposed and contrasty images. * accumulator threshold for the circle centers at the detection stage. The smaller it is, the more * false circles may be detected. Circles, corresponding to the larger accumulator values, will be * returned first. In the case of #HOUGH_GRADIENT_ALT algorithm, this is the circle "perfectness" measure. * The closer it to 1, the better shaped circles algorithm selects. In most cases 0.9 should be fine. * If you want get better detection of small circles, you may decrease it to 0.85, 0.8 or even less. * But then also try to limit the search range [minRadius, maxRadius] to avoid many false circles. * centers without finding the radius. #HOUGH_GRADIENT_ALT always computes circle radiuses. * * @see `+fitEllipse:`, `+minEnclosingCircle:center:radius:` */ + (void)HoughCircles:(Mat*)image circles:(Mat*)circles method:(HoughModes)method dp:(double)dp minDist:(double)minDist param1:(double)param1 NS_SWIFT_NAME(HoughCircles(image:circles:method:dp:minDist:param1:)); /** * Finds circles in a grayscale image using the Hough transform. * * The function finds circles in a grayscale image using a modification of the Hough transform. * * Example: : * INCLUDE: snippets/imgproc_HoughLinesCircles.cpp * * NOTE: Usually the function detects the centers of circles well. However, it may fail to find correct * radii. You can assist to the function by specifying the radius range ( minRadius and maxRadius ) if * you know it. Or, in the case of #HOUGH_GRADIENT method you may set maxRadius to a negative number * to return centers only without radius search, and find the correct radius using an additional procedure. * * It also helps to smooth image a bit unless it's already soft. For example, * GaussianBlur() with 7x7 kernel and 1.5x1.5 sigma or similar blurring may help. * * @param image 8-bit, single-channel, grayscale input image. * @param circles Output vector of found circles. Each vector is encoded as 3 or 4 element * floating-point vector `$$(x, y, radius)$$` or `$$(x, y, radius, votes)$$` . * @param method Detection method, see #HoughModes. The available methods are #HOUGH_GRADIENT and #HOUGH_GRADIENT_ALT. * @param dp Inverse ratio of the accumulator resolution to the image resolution. For example, if * dp=1 , the accumulator has the same resolution as the input image. If dp=2 , the accumulator has * half as big width and height. For #HOUGH_GRADIENT_ALT the recommended value is dp=1.5, * unless some small very circles need to be detected. * @param minDist Minimum distance between the centers of the detected circles. If the parameter is * too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is * too large, some circles may be missed. * it is the higher threshold of the two passed to the Canny edge detector (the lower one is twice smaller). * Note that #HOUGH_GRADIENT_ALT uses #Scharr algorithm to compute image derivatives, so the threshold value * shough normally be higher, such as 300 or normally exposed and contrasty images. * accumulator threshold for the circle centers at the detection stage. The smaller it is, the more * false circles may be detected. Circles, corresponding to the larger accumulator values, will be * returned first. In the case of #HOUGH_GRADIENT_ALT algorithm, this is the circle "perfectness" measure. * The closer it to 1, the better shaped circles algorithm selects. In most cases 0.9 should be fine. * If you want get better detection of small circles, you may decrease it to 0.85, 0.8 or even less. * But then also try to limit the search range [minRadius, maxRadius] to avoid many false circles. * centers without finding the radius. #HOUGH_GRADIENT_ALT always computes circle radiuses. * * @see `+fitEllipse:`, `+minEnclosingCircle:center:radius:` */ + (void)HoughCircles:(Mat*)image circles:(Mat*)circles method:(HoughModes)method dp:(double)dp minDist:(double)minDist NS_SWIFT_NAME(HoughCircles(image:circles:method:dp:minDist:)); // // void cv::erode(Mat src, Mat& dst, Mat kernel, Point anchor = Point(-1,-1), int iterations = 1, BorderTypes borderType = BORDER_CONSTANT, Scalar borderValue = morphologyDefaultBorderValue()) // /** * Erodes an image by using a specific structuring element. * * The function erodes the source image using the specified structuring element that determines the * shape of a pixel neighborhood over which the minimum is taken: * * `$$\texttt{dst} (x,y) = \min _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')$$` * * The function supports the in-place mode. Erosion can be applied several ( iterations ) times. In * case of multi-channel images, each channel is processed independently. * * @param src input image; the number of channels can be arbitrary, but the depth should be one of * CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. * @param dst output image of the same size and type as src. * @param kernel structuring element used for erosion; if `element=Mat()`, a `3 x 3` rectangular * structuring element is used. Kernel can be created using #getStructuringElement. * @param anchor position of the anchor within the element; default value (-1, -1) means that the * anchor is at the element center. * @param iterations number of times erosion is applied. * @param borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported. * @param borderValue border value in case of a constant border * @see `+dilate:dst:kernel:anchor:iterations:borderType:borderValue:`, `+morphologyEx:dst:op:kernel:anchor:iterations:borderType:borderValue:`, `+getStructuringElement:ksize:anchor:` */ + (void)erode:(Mat*)src dst:(Mat*)dst kernel:(Mat*)kernel anchor:(Point2i*)anchor iterations:(int)iterations borderType:(BorderTypes)borderType borderValue:(Scalar*)borderValue NS_SWIFT_NAME(erode(src:dst:kernel:anchor:iterations:borderType:borderValue:)); /** * Erodes an image by using a specific structuring element. * * The function erodes the source image using the specified structuring element that determines the * shape of a pixel neighborhood over which the minimum is taken: * * `$$\texttt{dst} (x,y) = \min _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')$$` * * The function supports the in-place mode. Erosion can be applied several ( iterations ) times. In * case of multi-channel images, each channel is processed independently. * * @param src input image; the number of channels can be arbitrary, but the depth should be one of * CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. * @param dst output image of the same size and type as src. * @param kernel structuring element used for erosion; if `element=Mat()`, a `3 x 3` rectangular * structuring element is used. Kernel can be created using #getStructuringElement. * @param anchor position of the anchor within the element; default value (-1, -1) means that the * anchor is at the element center. * @param iterations number of times erosion is applied. * @param borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported. * @see `+dilate:dst:kernel:anchor:iterations:borderType:borderValue:`, `+morphologyEx:dst:op:kernel:anchor:iterations:borderType:borderValue:`, `+getStructuringElement:ksize:anchor:` */ + (void)erode:(Mat*)src dst:(Mat*)dst kernel:(Mat*)kernel anchor:(Point2i*)anchor iterations:(int)iterations borderType:(BorderTypes)borderType NS_SWIFT_NAME(erode(src:dst:kernel:anchor:iterations:borderType:)); /** * Erodes an image by using a specific structuring element. * * The function erodes the source image using the specified structuring element that determines the * shape of a pixel neighborhood over which the minimum is taken: * * `$$\texttt{dst} (x,y) = \min _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')$$` * * The function supports the in-place mode. Erosion can be applied several ( iterations ) times. In * case of multi-channel images, each channel is processed independently. * * @param src input image; the number of channels can be arbitrary, but the depth should be one of * CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. * @param dst output image of the same size and type as src. * @param kernel structuring element used for erosion; if `element=Mat()`, a `3 x 3` rectangular * structuring element is used. Kernel can be created using #getStructuringElement. * @param anchor position of the anchor within the element; default value (-1, -1) means that the * anchor is at the element center. * @param iterations number of times erosion is applied. * @see `+dilate:dst:kernel:anchor:iterations:borderType:borderValue:`, `+morphologyEx:dst:op:kernel:anchor:iterations:borderType:borderValue:`, `+getStructuringElement:ksize:anchor:` */ + (void)erode:(Mat*)src dst:(Mat*)dst kernel:(Mat*)kernel anchor:(Point2i*)anchor iterations:(int)iterations NS_SWIFT_NAME(erode(src:dst:kernel:anchor:iterations:)); /** * Erodes an image by using a specific structuring element. * * The function erodes the source image using the specified structuring element that determines the * shape of a pixel neighborhood over which the minimum is taken: * * `$$\texttt{dst} (x,y) = \min _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')$$` * * The function supports the in-place mode. Erosion can be applied several ( iterations ) times. In * case of multi-channel images, each channel is processed independently. * * @param src input image; the number of channels can be arbitrary, but the depth should be one of * CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. * @param dst output image of the same size and type as src. * @param kernel structuring element used for erosion; if `element=Mat()`, a `3 x 3` rectangular * structuring element is used. Kernel can be created using #getStructuringElement. * @param anchor position of the anchor within the element; default value (-1, -1) means that the * anchor is at the element center. * @see `+dilate:dst:kernel:anchor:iterations:borderType:borderValue:`, `+morphologyEx:dst:op:kernel:anchor:iterations:borderType:borderValue:`, `+getStructuringElement:ksize:anchor:` */ + (void)erode:(Mat*)src dst:(Mat*)dst kernel:(Mat*)kernel anchor:(Point2i*)anchor NS_SWIFT_NAME(erode(src:dst:kernel:anchor:)); /** * Erodes an image by using a specific structuring element. * * The function erodes the source image using the specified structuring element that determines the * shape of a pixel neighborhood over which the minimum is taken: * * `$$\texttt{dst} (x,y) = \min _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')$$` * * The function supports the in-place mode. Erosion can be applied several ( iterations ) times. In * case of multi-channel images, each channel is processed independently. * * @param src input image; the number of channels can be arbitrary, but the depth should be one of * CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. * @param dst output image of the same size and type as src. * @param kernel structuring element used for erosion; if `element=Mat()`, a `3 x 3` rectangular * structuring element is used. Kernel can be created using #getStructuringElement. * anchor is at the element center. * @see `+dilate:dst:kernel:anchor:iterations:borderType:borderValue:`, `+morphologyEx:dst:op:kernel:anchor:iterations:borderType:borderValue:`, `+getStructuringElement:ksize:anchor:` */ + (void)erode:(Mat*)src dst:(Mat*)dst kernel:(Mat*)kernel NS_SWIFT_NAME(erode(src:dst:kernel:)); // // void cv::dilate(Mat src, Mat& dst, Mat kernel, Point anchor = Point(-1,-1), int iterations = 1, BorderTypes borderType = BORDER_CONSTANT, Scalar borderValue = morphologyDefaultBorderValue()) // /** * Dilates an image by using a specific structuring element. * * The function dilates the source image using the specified structuring element that determines the * shape of a pixel neighborhood over which the maximum is taken: * `$$\texttt{dst} (x,y) = \max _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')$$` * * The function supports the in-place mode. Dilation can be applied several ( iterations ) times. In * case of multi-channel images, each channel is processed independently. * * @param src input image; the number of channels can be arbitrary, but the depth should be one of * CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. * @param dst output image of the same size and type as src. * @param kernel structuring element used for dilation; if elemenat=Mat(), a 3 x 3 rectangular * structuring element is used. Kernel can be created using #getStructuringElement * @param anchor position of the anchor within the element; default value (-1, -1) means that the * anchor is at the element center. * @param iterations number of times dilation is applied. * @param borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not suported. * @param borderValue border value in case of a constant border * @see `+erode:dst:kernel:anchor:iterations:borderType:borderValue:`, `+morphologyEx:dst:op:kernel:anchor:iterations:borderType:borderValue:`, `+getStructuringElement:ksize:anchor:` */ + (void)dilate:(Mat*)src dst:(Mat*)dst kernel:(Mat*)kernel anchor:(Point2i*)anchor iterations:(int)iterations borderType:(BorderTypes)borderType borderValue:(Scalar*)borderValue NS_SWIFT_NAME(dilate(src:dst:kernel:anchor:iterations:borderType:borderValue:)); /** * Dilates an image by using a specific structuring element. * * The function dilates the source image using the specified structuring element that determines the * shape of a pixel neighborhood over which the maximum is taken: * `$$\texttt{dst} (x,y) = \max _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')$$` * * The function supports the in-place mode. Dilation can be applied several ( iterations ) times. In * case of multi-channel images, each channel is processed independently. * * @param src input image; the number of channels can be arbitrary, but the depth should be one of * CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. * @param dst output image of the same size and type as src. * @param kernel structuring element used for dilation; if elemenat=Mat(), a 3 x 3 rectangular * structuring element is used. Kernel can be created using #getStructuringElement * @param anchor position of the anchor within the element; default value (-1, -1) means that the * anchor is at the element center. * @param iterations number of times dilation is applied. * @param borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not suported. * @see `+erode:dst:kernel:anchor:iterations:borderType:borderValue:`, `+morphologyEx:dst:op:kernel:anchor:iterations:borderType:borderValue:`, `+getStructuringElement:ksize:anchor:` */ + (void)dilate:(Mat*)src dst:(Mat*)dst kernel:(Mat*)kernel anchor:(Point2i*)anchor iterations:(int)iterations borderType:(BorderTypes)borderType NS_SWIFT_NAME(dilate(src:dst:kernel:anchor:iterations:borderType:)); /** * Dilates an image by using a specific structuring element. * * The function dilates the source image using the specified structuring element that determines the * shape of a pixel neighborhood over which the maximum is taken: * `$$\texttt{dst} (x,y) = \max _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')$$` * * The function supports the in-place mode. Dilation can be applied several ( iterations ) times. In * case of multi-channel images, each channel is processed independently. * * @param src input image; the number of channels can be arbitrary, but the depth should be one of * CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. * @param dst output image of the same size and type as src. * @param kernel structuring element used for dilation; if elemenat=Mat(), a 3 x 3 rectangular * structuring element is used. Kernel can be created using #getStructuringElement * @param anchor position of the anchor within the element; default value (-1, -1) means that the * anchor is at the element center. * @param iterations number of times dilation is applied. * @see `+erode:dst:kernel:anchor:iterations:borderType:borderValue:`, `+morphologyEx:dst:op:kernel:anchor:iterations:borderType:borderValue:`, `+getStructuringElement:ksize:anchor:` */ + (void)dilate:(Mat*)src dst:(Mat*)dst kernel:(Mat*)kernel anchor:(Point2i*)anchor iterations:(int)iterations NS_SWIFT_NAME(dilate(src:dst:kernel:anchor:iterations:)); /** * Dilates an image by using a specific structuring element. * * The function dilates the source image using the specified structuring element that determines the * shape of a pixel neighborhood over which the maximum is taken: * `$$\texttt{dst} (x,y) = \max _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')$$` * * The function supports the in-place mode. Dilation can be applied several ( iterations ) times. In * case of multi-channel images, each channel is processed independently. * * @param src input image; the number of channels can be arbitrary, but the depth should be one of * CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. * @param dst output image of the same size and type as src. * @param kernel structuring element used for dilation; if elemenat=Mat(), a 3 x 3 rectangular * structuring element is used. Kernel can be created using #getStructuringElement * @param anchor position of the anchor within the element; default value (-1, -1) means that the * anchor is at the element center. * @see `+erode:dst:kernel:anchor:iterations:borderType:borderValue:`, `+morphologyEx:dst:op:kernel:anchor:iterations:borderType:borderValue:`, `+getStructuringElement:ksize:anchor:` */ + (void)dilate:(Mat*)src dst:(Mat*)dst kernel:(Mat*)kernel anchor:(Point2i*)anchor NS_SWIFT_NAME(dilate(src:dst:kernel:anchor:)); /** * Dilates an image by using a specific structuring element. * * The function dilates the source image using the specified structuring element that determines the * shape of a pixel neighborhood over which the maximum is taken: * `$$\texttt{dst} (x,y) = \max _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')$$` * * The function supports the in-place mode. Dilation can be applied several ( iterations ) times. In * case of multi-channel images, each channel is processed independently. * * @param src input image; the number of channels can be arbitrary, but the depth should be one of * CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. * @param dst output image of the same size and type as src. * @param kernel structuring element used for dilation; if elemenat=Mat(), a 3 x 3 rectangular * structuring element is used. Kernel can be created using #getStructuringElement * anchor is at the element center. * @see `+erode:dst:kernel:anchor:iterations:borderType:borderValue:`, `+morphologyEx:dst:op:kernel:anchor:iterations:borderType:borderValue:`, `+getStructuringElement:ksize:anchor:` */ + (void)dilate:(Mat*)src dst:(Mat*)dst kernel:(Mat*)kernel NS_SWIFT_NAME(dilate(src:dst:kernel:)); // // void cv::morphologyEx(Mat src, Mat& dst, MorphTypes op, Mat kernel, Point anchor = Point(-1,-1), int iterations = 1, BorderTypes borderType = BORDER_CONSTANT, Scalar borderValue = morphologyDefaultBorderValue()) // /** * Performs advanced morphological transformations. * * The function cv::morphologyEx can perform advanced morphological transformations using an erosion and dilation as * basic operations. * * Any of the operations can be done in-place. In case of multi-channel images, each channel is * processed independently. * * @param src Source image. The number of channels can be arbitrary. The depth should be one of * CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. * @param dst Destination image of the same size and type as source image. * @param op Type of a morphological operation, see #MorphTypes * @param kernel Structuring element. It can be created using #getStructuringElement. * @param anchor Anchor position with the kernel. Negative values mean that the anchor is at the * kernel center. * @param iterations Number of times erosion and dilation are applied. * @param borderType Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported. * @param borderValue Border value in case of a constant border. The default value has a special * meaning. * @see `+dilate:dst:kernel:anchor:iterations:borderType:borderValue:`, `+erode:dst:kernel:anchor:iterations:borderType:borderValue:`, `+getStructuringElement:ksize:anchor:` * NOTE: The number of iterations is the number of times erosion or dilatation operation will be applied. * For instance, an opening operation (#MORPH_OPEN) with two iterations is equivalent to apply * successively: erode -> erode -> dilate -> dilate (and not erode -> dilate -> erode -> dilate). */ + (void)morphologyEx:(Mat*)src dst:(Mat*)dst op:(MorphTypes)op kernel:(Mat*)kernel anchor:(Point2i*)anchor iterations:(int)iterations borderType:(BorderTypes)borderType borderValue:(Scalar*)borderValue NS_SWIFT_NAME(morphologyEx(src:dst:op:kernel:anchor:iterations:borderType:borderValue:)); /** * Performs advanced morphological transformations. * * The function cv::morphologyEx can perform advanced morphological transformations using an erosion and dilation as * basic operations. * * Any of the operations can be done in-place. In case of multi-channel images, each channel is * processed independently. * * @param src Source image. The number of channels can be arbitrary. The depth should be one of * CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. * @param dst Destination image of the same size and type as source image. * @param op Type of a morphological operation, see #MorphTypes * @param kernel Structuring element. It can be created using #getStructuringElement. * @param anchor Anchor position with the kernel. Negative values mean that the anchor is at the * kernel center. * @param iterations Number of times erosion and dilation are applied. * @param borderType Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported. * meaning. * @see `+dilate:dst:kernel:anchor:iterations:borderType:borderValue:`, `+erode:dst:kernel:anchor:iterations:borderType:borderValue:`, `+getStructuringElement:ksize:anchor:` * NOTE: The number of iterations is the number of times erosion or dilatation operation will be applied. * For instance, an opening operation (#MORPH_OPEN) with two iterations is equivalent to apply * successively: erode -> erode -> dilate -> dilate (and not erode -> dilate -> erode -> dilate). */ + (void)morphologyEx:(Mat*)src dst:(Mat*)dst op:(MorphTypes)op kernel:(Mat*)kernel anchor:(Point2i*)anchor iterations:(int)iterations borderType:(BorderTypes)borderType NS_SWIFT_NAME(morphologyEx(src:dst:op:kernel:anchor:iterations:borderType:)); /** * Performs advanced morphological transformations. * * The function cv::morphologyEx can perform advanced morphological transformations using an erosion and dilation as * basic operations. * * Any of the operations can be done in-place. In case of multi-channel images, each channel is * processed independently. * * @param src Source image. The number of channels can be arbitrary. The depth should be one of * CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. * @param dst Destination image of the same size and type as source image. * @param op Type of a morphological operation, see #MorphTypes * @param kernel Structuring element. It can be created using #getStructuringElement. * @param anchor Anchor position with the kernel. Negative values mean that the anchor is at the * kernel center. * @param iterations Number of times erosion and dilation are applied. * meaning. * @see `+dilate:dst:kernel:anchor:iterations:borderType:borderValue:`, `+erode:dst:kernel:anchor:iterations:borderType:borderValue:`, `+getStructuringElement:ksize:anchor:` * NOTE: The number of iterations is the number of times erosion or dilatation operation will be applied. * For instance, an opening operation (#MORPH_OPEN) with two iterations is equivalent to apply * successively: erode -> erode -> dilate -> dilate (and not erode -> dilate -> erode -> dilate). */ + (void)morphologyEx:(Mat*)src dst:(Mat*)dst op:(MorphTypes)op kernel:(Mat*)kernel anchor:(Point2i*)anchor iterations:(int)iterations NS_SWIFT_NAME(morphologyEx(src:dst:op:kernel:anchor:iterations:)); /** * Performs advanced morphological transformations. * * The function cv::morphologyEx can perform advanced morphological transformations using an erosion and dilation as * basic operations. * * Any of the operations can be done in-place. In case of multi-channel images, each channel is * processed independently. * * @param src Source image. The number of channels can be arbitrary. The depth should be one of * CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. * @param dst Destination image of the same size and type as source image. * @param op Type of a morphological operation, see #MorphTypes * @param kernel Structuring element. It can be created using #getStructuringElement. * @param anchor Anchor position with the kernel. Negative values mean that the anchor is at the * kernel center. * meaning. * @see `+dilate:dst:kernel:anchor:iterations:borderType:borderValue:`, `+erode:dst:kernel:anchor:iterations:borderType:borderValue:`, `+getStructuringElement:ksize:anchor:` * NOTE: The number of iterations is the number of times erosion or dilatation operation will be applied. * For instance, an opening operation (#MORPH_OPEN) with two iterations is equivalent to apply * successively: erode -> erode -> dilate -> dilate (and not erode -> dilate -> erode -> dilate). */ + (void)morphologyEx:(Mat*)src dst:(Mat*)dst op:(MorphTypes)op kernel:(Mat*)kernel anchor:(Point2i*)anchor NS_SWIFT_NAME(morphologyEx(src:dst:op:kernel:anchor:)); /** * Performs advanced morphological transformations. * * The function cv::morphologyEx can perform advanced morphological transformations using an erosion and dilation as * basic operations. * * Any of the operations can be done in-place. In case of multi-channel images, each channel is * processed independently. * * @param src Source image. The number of channels can be arbitrary. The depth should be one of * CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. * @param dst Destination image of the same size and type as source image. * @param op Type of a morphological operation, see #MorphTypes * @param kernel Structuring element. It can be created using #getStructuringElement. * kernel center. * meaning. * @see `+dilate:dst:kernel:anchor:iterations:borderType:borderValue:`, `+erode:dst:kernel:anchor:iterations:borderType:borderValue:`, `+getStructuringElement:ksize:anchor:` * NOTE: The number of iterations is the number of times erosion or dilatation operation will be applied. * For instance, an opening operation (#MORPH_OPEN) with two iterations is equivalent to apply * successively: erode -> erode -> dilate -> dilate (and not erode -> dilate -> erode -> dilate). */ + (void)morphologyEx:(Mat*)src dst:(Mat*)dst op:(MorphTypes)op kernel:(Mat*)kernel NS_SWIFT_NAME(morphologyEx(src:dst:op:kernel:)); // // void cv::resize(Mat src, Mat& dst, Size dsize, double fx = 0, double fy = 0, int interpolation = INTER_LINEAR) // /** * Resizes an image. * * The function resize resizes the image src down to or up to the specified size. Note that the * initial dst type or size are not taken into account. Instead, the size and type are derived from * the `src`,`dsize`,`fx`, and `fy`. If you want to resize src so that it fits the pre-created dst, * you may call the function as follows: * * // explicitly specify dsize=dst.size(); fx and fy will be computed from that. * resize(src, dst, dst.size(), 0, 0, interpolation); * * If you want to decimate the image by factor of 2 in each direction, you can call the function this * way: * * // specify fx and fy and let the function compute the destination image size. * resize(src, dst, Size(), 0.5, 0.5, interpolation); * * To shrink an image, it will generally look best with #INTER_AREA interpolation, whereas to * enlarge an image, it will generally look best with #INTER_CUBIC (slow) or #INTER_LINEAR * (faster but still looks OK). * * @param src input image. * @param dst output image; it has the size dsize (when it is non-zero) or the size computed from * src.size(), fx, and fy; the type of dst is the same as of src. * @param dsize output image size; if it equals zero (`None` in Python), it is computed as: * `$$\texttt{dsize = Size(round(fx*src.cols), round(fy*src.rows))}$$` * Either dsize or both fx and fy must be non-zero. * @param fx scale factor along the horizontal axis; when it equals 0, it is computed as * `$$\texttt{(double)dsize.width/src.cols}$$` * @param fy scale factor along the vertical axis; when it equals 0, it is computed as * `$$\texttt{(double)dsize.height/src.rows}$$` * @param interpolation interpolation method, see #InterpolationFlags * * @see `+warpAffine:dst:M:dsize:flags:borderMode:borderValue:`, `+warpPerspective:dst:M:dsize:flags:borderMode:borderValue:`, `+remap:dst:map1:map2:interpolation:borderMode:borderValue:` */ + (void)resize:(Mat*)src dst:(Mat*)dst dsize:(Size2i*)dsize fx:(double)fx fy:(double)fy interpolation:(int)interpolation NS_SWIFT_NAME(resize(src:dst:dsize:fx:fy:interpolation:)); /** * Resizes an image. * * The function resize resizes the image src down to or up to the specified size. Note that the * initial dst type or size are not taken into account. Instead, the size and type are derived from * the `src`,`dsize`,`fx`, and `fy`. If you want to resize src so that it fits the pre-created dst, * you may call the function as follows: * * // explicitly specify dsize=dst.size(); fx and fy will be computed from that. * resize(src, dst, dst.size(), 0, 0, interpolation); * * If you want to decimate the image by factor of 2 in each direction, you can call the function this * way: * * // specify fx and fy and let the function compute the destination image size. * resize(src, dst, Size(), 0.5, 0.5, interpolation); * * To shrink an image, it will generally look best with #INTER_AREA interpolation, whereas to * enlarge an image, it will generally look best with #INTER_CUBIC (slow) or #INTER_LINEAR * (faster but still looks OK). * * @param src input image. * @param dst output image; it has the size dsize (when it is non-zero) or the size computed from * src.size(), fx, and fy; the type of dst is the same as of src. * @param dsize output image size; if it equals zero (`None` in Python), it is computed as: * `$$\texttt{dsize = Size(round(fx*src.cols), round(fy*src.rows))}$$` * Either dsize or both fx and fy must be non-zero. * @param fx scale factor along the horizontal axis; when it equals 0, it is computed as * `$$\texttt{(double)dsize.width/src.cols}$$` * @param fy scale factor along the vertical axis; when it equals 0, it is computed as * `$$\texttt{(double)dsize.height/src.rows}$$` * * @see `+warpAffine:dst:M:dsize:flags:borderMode:borderValue:`, `+warpPerspective:dst:M:dsize:flags:borderMode:borderValue:`, `+remap:dst:map1:map2:interpolation:borderMode:borderValue:` */ + (void)resize:(Mat*)src dst:(Mat*)dst dsize:(Size2i*)dsize fx:(double)fx fy:(double)fy NS_SWIFT_NAME(resize(src:dst:dsize:fx:fy:)); /** * Resizes an image. * * The function resize resizes the image src down to or up to the specified size. Note that the * initial dst type or size are not taken into account. Instead, the size and type are derived from * the `src`,`dsize`,`fx`, and `fy`. If you want to resize src so that it fits the pre-created dst, * you may call the function as follows: * * // explicitly specify dsize=dst.size(); fx and fy will be computed from that. * resize(src, dst, dst.size(), 0, 0, interpolation); * * If you want to decimate the image by factor of 2 in each direction, you can call the function this * way: * * // specify fx and fy and let the function compute the destination image size. * resize(src, dst, Size(), 0.5, 0.5, interpolation); * * To shrink an image, it will generally look best with #INTER_AREA interpolation, whereas to * enlarge an image, it will generally look best with #INTER_CUBIC (slow) or #INTER_LINEAR * (faster but still looks OK). * * @param src input image. * @param dst output image; it has the size dsize (when it is non-zero) or the size computed from * src.size(), fx, and fy; the type of dst is the same as of src. * @param dsize output image size; if it equals zero (`None` in Python), it is computed as: * `$$\texttt{dsize = Size(round(fx*src.cols), round(fy*src.rows))}$$` * Either dsize or both fx and fy must be non-zero. * @param fx scale factor along the horizontal axis; when it equals 0, it is computed as * `$$\texttt{(double)dsize.width/src.cols}$$` * `$$\texttt{(double)dsize.height/src.rows}$$` * * @see `+warpAffine:dst:M:dsize:flags:borderMode:borderValue:`, `+warpPerspective:dst:M:dsize:flags:borderMode:borderValue:`, `+remap:dst:map1:map2:interpolation:borderMode:borderValue:` */ + (void)resize:(Mat*)src dst:(Mat*)dst dsize:(Size2i*)dsize fx:(double)fx NS_SWIFT_NAME(resize(src:dst:dsize:fx:)); /** * Resizes an image. * * The function resize resizes the image src down to or up to the specified size. Note that the * initial dst type or size are not taken into account. Instead, the size and type are derived from * the `src`,`dsize`,`fx`, and `fy`. If you want to resize src so that it fits the pre-created dst, * you may call the function as follows: * * // explicitly specify dsize=dst.size(); fx and fy will be computed from that. * resize(src, dst, dst.size(), 0, 0, interpolation); * * If you want to decimate the image by factor of 2 in each direction, you can call the function this * way: * * // specify fx and fy and let the function compute the destination image size. * resize(src, dst, Size(), 0.5, 0.5, interpolation); * * To shrink an image, it will generally look best with #INTER_AREA interpolation, whereas to * enlarge an image, it will generally look best with #INTER_CUBIC (slow) or #INTER_LINEAR * (faster but still looks OK). * * @param src input image. * @param dst output image; it has the size dsize (when it is non-zero) or the size computed from * src.size(), fx, and fy; the type of dst is the same as of src. * @param dsize output image size; if it equals zero (`None` in Python), it is computed as: * `$$\texttt{dsize = Size(round(fx*src.cols), round(fy*src.rows))}$$` * Either dsize or both fx and fy must be non-zero. * `$$\texttt{(double)dsize.width/src.cols}$$` * `$$\texttt{(double)dsize.height/src.rows}$$` * * @see `+warpAffine:dst:M:dsize:flags:borderMode:borderValue:`, `+warpPerspective:dst:M:dsize:flags:borderMode:borderValue:`, `+remap:dst:map1:map2:interpolation:borderMode:borderValue:` */ + (void)resize:(Mat*)src dst:(Mat*)dst dsize:(Size2i*)dsize NS_SWIFT_NAME(resize(src:dst:dsize:)); // // void cv::warpAffine(Mat src, Mat& dst, Mat M, Size dsize, int flags = INTER_LINEAR, BorderTypes borderMode = BORDER_CONSTANT, Scalar borderValue = Scalar()) // /** * Applies an affine transformation to an image. * * The function warpAffine transforms the source image using the specified matrix: * * `$$\texttt{dst} (x,y) = \texttt{src} ( \texttt{M} _{11} x + \texttt{M} _{12} y + \texttt{M} _{13}, \texttt{M} _{21} x + \texttt{M} _{22} y + \texttt{M} _{23})$$` * * when the flag #WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted * with #invertAffineTransform and then put in the formula above instead of M. The function cannot * operate in-place. * * @param src input image. * @param dst output image that has the size dsize and the same type as src . * @param M `$$2\times 3$$` transformation matrix. * @param dsize size of the output image. * @param flags combination of interpolation methods (see #InterpolationFlags) and the optional * flag #WARP_INVERSE_MAP that means that M is the inverse transformation ( * `$$\texttt{dst}\rightarrow\texttt{src}$$` ). * @param borderMode pixel extrapolation method (see #BorderTypes); when * borderMode=#BORDER_TRANSPARENT, it means that the pixels in the destination image corresponding to * the "outliers" in the source image are not modified by the function. * @param borderValue value used in case of a constant border; by default, it is 0. * * @see `+warpPerspective:dst:M:dsize:flags:borderMode:borderValue:`, `+resize:dst:dsize:fx:fy:interpolation:`, `+remap:dst:map1:map2:interpolation:borderMode:borderValue:`, `+getRectSubPix:patchSize:center:patch:patchType:`, `transform` */ + (void)warpAffine:(Mat*)src dst:(Mat*)dst M:(Mat*)M dsize:(Size2i*)dsize flags:(int)flags borderMode:(BorderTypes)borderMode borderValue:(Scalar*)borderValue NS_SWIFT_NAME(warpAffine(src:dst:M:dsize:flags:borderMode:borderValue:)); /** * Applies an affine transformation to an image. * * The function warpAffine transforms the source image using the specified matrix: * * `$$\texttt{dst} (x,y) = \texttt{src} ( \texttt{M} _{11} x + \texttt{M} _{12} y + \texttt{M} _{13}, \texttt{M} _{21} x + \texttt{M} _{22} y + \texttt{M} _{23})$$` * * when the flag #WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted * with #invertAffineTransform and then put in the formula above instead of M. The function cannot * operate in-place. * * @param src input image. * @param dst output image that has the size dsize and the same type as src . * @param M `$$2\times 3$$` transformation matrix. * @param dsize size of the output image. * @param flags combination of interpolation methods (see #InterpolationFlags) and the optional * flag #WARP_INVERSE_MAP that means that M is the inverse transformation ( * `$$\texttt{dst}\rightarrow\texttt{src}$$` ). * @param borderMode pixel extrapolation method (see #BorderTypes); when * borderMode=#BORDER_TRANSPARENT, it means that the pixels in the destination image corresponding to * the "outliers" in the source image are not modified by the function. * * @see `+warpPerspective:dst:M:dsize:flags:borderMode:borderValue:`, `+resize:dst:dsize:fx:fy:interpolation:`, `+remap:dst:map1:map2:interpolation:borderMode:borderValue:`, `+getRectSubPix:patchSize:center:patch:patchType:`, `transform` */ + (void)warpAffine:(Mat*)src dst:(Mat*)dst M:(Mat*)M dsize:(Size2i*)dsize flags:(int)flags borderMode:(BorderTypes)borderMode NS_SWIFT_NAME(warpAffine(src:dst:M:dsize:flags:borderMode:)); /** * Applies an affine transformation to an image. * * The function warpAffine transforms the source image using the specified matrix: * * `$$\texttt{dst} (x,y) = \texttt{src} ( \texttt{M} _{11} x + \texttt{M} _{12} y + \texttt{M} _{13}, \texttt{M} _{21} x + \texttt{M} _{22} y + \texttt{M} _{23})$$` * * when the flag #WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted * with #invertAffineTransform and then put in the formula above instead of M. The function cannot * operate in-place. * * @param src input image. * @param dst output image that has the size dsize and the same type as src . * @param M `$$2\times 3$$` transformation matrix. * @param dsize size of the output image. * @param flags combination of interpolation methods (see #InterpolationFlags) and the optional * flag #WARP_INVERSE_MAP that means that M is the inverse transformation ( * `$$\texttt{dst}\rightarrow\texttt{src}$$` ). * borderMode=#BORDER_TRANSPARENT, it means that the pixels in the destination image corresponding to * the "outliers" in the source image are not modified by the function. * * @see `+warpPerspective:dst:M:dsize:flags:borderMode:borderValue:`, `+resize:dst:dsize:fx:fy:interpolation:`, `+remap:dst:map1:map2:interpolation:borderMode:borderValue:`, `+getRectSubPix:patchSize:center:patch:patchType:`, `transform` */ + (void)warpAffine:(Mat*)src dst:(Mat*)dst M:(Mat*)M dsize:(Size2i*)dsize flags:(int)flags NS_SWIFT_NAME(warpAffine(src:dst:M:dsize:flags:)); /** * Applies an affine transformation to an image. * * The function warpAffine transforms the source image using the specified matrix: * * `$$\texttt{dst} (x,y) = \texttt{src} ( \texttt{M} _{11} x + \texttt{M} _{12} y + \texttt{M} _{13}, \texttt{M} _{21} x + \texttt{M} _{22} y + \texttt{M} _{23})$$` * * when the flag #WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted * with #invertAffineTransform and then put in the formula above instead of M. The function cannot * operate in-place. * * @param src input image. * @param dst output image that has the size dsize and the same type as src . * @param M `$$2\times 3$$` transformation matrix. * @param dsize size of the output image. * flag #WARP_INVERSE_MAP that means that M is the inverse transformation ( * `$$\texttt{dst}\rightarrow\texttt{src}$$` ). * borderMode=#BORDER_TRANSPARENT, it means that the pixels in the destination image corresponding to * the "outliers" in the source image are not modified by the function. * * @see `+warpPerspective:dst:M:dsize:flags:borderMode:borderValue:`, `+resize:dst:dsize:fx:fy:interpolation:`, `+remap:dst:map1:map2:interpolation:borderMode:borderValue:`, `+getRectSubPix:patchSize:center:patch:patchType:`, `transform` */ + (void)warpAffine:(Mat*)src dst:(Mat*)dst M:(Mat*)M dsize:(Size2i*)dsize NS_SWIFT_NAME(warpAffine(src:dst:M:dsize:)); // // void cv::warpPerspective(Mat src, Mat& dst, Mat M, Size dsize, int flags = INTER_LINEAR, BorderTypes borderMode = BORDER_CONSTANT, Scalar borderValue = Scalar()) // /** * Applies a perspective transformation to an image. * * The function warpPerspective transforms the source image using the specified matrix: * * `$$\texttt{dst} (x,y) = \texttt{src} \left ( \frac{M_{11} x + M_{12} y + M_{13}}{M_{31} x + M_{32} y + M_{33}} , * \frac{M_{21} x + M_{22} y + M_{23}}{M_{31} x + M_{32} y + M_{33}} \right )$$` * * when the flag #WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted with invert * and then put in the formula above instead of M. The function cannot operate in-place. * * @param src input image. * @param dst output image that has the size dsize and the same type as src . * @param M `$$3\times 3$$` transformation matrix. * @param dsize size of the output image. * @param flags combination of interpolation methods (#INTER_LINEAR or #INTER_NEAREST) and the * optional flag #WARP_INVERSE_MAP, that sets M as the inverse transformation ( * `$$\texttt{dst}\rightarrow\texttt{src}$$` ). * @param borderMode pixel extrapolation method (#BORDER_CONSTANT or #BORDER_REPLICATE). * @param borderValue value used in case of a constant border; by default, it equals 0. * * @see `+warpAffine:dst:M:dsize:flags:borderMode:borderValue:`, `+resize:dst:dsize:fx:fy:interpolation:`, `+remap:dst:map1:map2:interpolation:borderMode:borderValue:`, `+getRectSubPix:patchSize:center:patch:patchType:`, `perspectiveTransform` */ + (void)warpPerspective:(Mat*)src dst:(Mat*)dst M:(Mat*)M dsize:(Size2i*)dsize flags:(int)flags borderMode:(BorderTypes)borderMode borderValue:(Scalar*)borderValue NS_SWIFT_NAME(warpPerspective(src:dst:M:dsize:flags:borderMode:borderValue:)); /** * Applies a perspective transformation to an image. * * The function warpPerspective transforms the source image using the specified matrix: * * `$$\texttt{dst} (x,y) = \texttt{src} \left ( \frac{M_{11} x + M_{12} y + M_{13}}{M_{31} x + M_{32} y + M_{33}} , * \frac{M_{21} x + M_{22} y + M_{23}}{M_{31} x + M_{32} y + M_{33}} \right )$$` * * when the flag #WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted with invert * and then put in the formula above instead of M. The function cannot operate in-place. * * @param src input image. * @param dst output image that has the size dsize and the same type as src . * @param M `$$3\times 3$$` transformation matrix. * @param dsize size of the output image. * @param flags combination of interpolation methods (#INTER_LINEAR or #INTER_NEAREST) and the * optional flag #WARP_INVERSE_MAP, that sets M as the inverse transformation ( * `$$\texttt{dst}\rightarrow\texttt{src}$$` ). * @param borderMode pixel extrapolation method (#BORDER_CONSTANT or #BORDER_REPLICATE). * * @see `+warpAffine:dst:M:dsize:flags:borderMode:borderValue:`, `+resize:dst:dsize:fx:fy:interpolation:`, `+remap:dst:map1:map2:interpolation:borderMode:borderValue:`, `+getRectSubPix:patchSize:center:patch:patchType:`, `perspectiveTransform` */ + (void)warpPerspective:(Mat*)src dst:(Mat*)dst M:(Mat*)M dsize:(Size2i*)dsize flags:(int)flags borderMode:(BorderTypes)borderMode NS_SWIFT_NAME(warpPerspective(src:dst:M:dsize:flags:borderMode:)); /** * Applies a perspective transformation to an image. * * The function warpPerspective transforms the source image using the specified matrix: * * `$$\texttt{dst} (x,y) = \texttt{src} \left ( \frac{M_{11} x + M_{12} y + M_{13}}{M_{31} x + M_{32} y + M_{33}} , * \frac{M_{21} x + M_{22} y + M_{23}}{M_{31} x + M_{32} y + M_{33}} \right )$$` * * when the flag #WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted with invert * and then put in the formula above instead of M. The function cannot operate in-place. * * @param src input image. * @param dst output image that has the size dsize and the same type as src . * @param M `$$3\times 3$$` transformation matrix. * @param dsize size of the output image. * @param flags combination of interpolation methods (#INTER_LINEAR or #INTER_NEAREST) and the * optional flag #WARP_INVERSE_MAP, that sets M as the inverse transformation ( * `$$\texttt{dst}\rightarrow\texttt{src}$$` ). * * @see `+warpAffine:dst:M:dsize:flags:borderMode:borderValue:`, `+resize:dst:dsize:fx:fy:interpolation:`, `+remap:dst:map1:map2:interpolation:borderMode:borderValue:`, `+getRectSubPix:patchSize:center:patch:patchType:`, `perspectiveTransform` */ + (void)warpPerspective:(Mat*)src dst:(Mat*)dst M:(Mat*)M dsize:(Size2i*)dsize flags:(int)flags NS_SWIFT_NAME(warpPerspective(src:dst:M:dsize:flags:)); /** * Applies a perspective transformation to an image. * * The function warpPerspective transforms the source image using the specified matrix: * * `$$\texttt{dst} (x,y) = \texttt{src} \left ( \frac{M_{11} x + M_{12} y + M_{13}}{M_{31} x + M_{32} y + M_{33}} , * \frac{M_{21} x + M_{22} y + M_{23}}{M_{31} x + M_{32} y + M_{33}} \right )$$` * * when the flag #WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted with invert * and then put in the formula above instead of M. The function cannot operate in-place. * * @param src input image. * @param dst output image that has the size dsize and the same type as src . * @param M `$$3\times 3$$` transformation matrix. * @param dsize size of the output image. * optional flag #WARP_INVERSE_MAP, that sets M as the inverse transformation ( * `$$\texttt{dst}\rightarrow\texttt{src}$$` ). * * @see `+warpAffine:dst:M:dsize:flags:borderMode:borderValue:`, `+resize:dst:dsize:fx:fy:interpolation:`, `+remap:dst:map1:map2:interpolation:borderMode:borderValue:`, `+getRectSubPix:patchSize:center:patch:patchType:`, `perspectiveTransform` */ + (void)warpPerspective:(Mat*)src dst:(Mat*)dst M:(Mat*)M dsize:(Size2i*)dsize NS_SWIFT_NAME(warpPerspective(src:dst:M:dsize:)); // // void cv::remap(Mat src, Mat& dst, Mat map1, Mat map2, int interpolation, BorderTypes borderMode = BORDER_CONSTANT, Scalar borderValue = Scalar()) // /** * Applies a generic geometrical transformation to an image. * * The function remap transforms the source image using the specified map: * * `$$\texttt{dst} (x,y) = \texttt{src} (map_x(x,y),map_y(x,y))$$` * * where values of pixels with non-integer coordinates are computed using one of available * interpolation methods. `$$map_x$$` and `$$map_y$$` can be encoded as separate floating-point maps * in `$$map_1$$` and `$$map_2$$` respectively, or interleaved floating-point maps of `$$(x,y)$$` in * `$$map_1$$`, or fixed-point maps created by using #convertMaps. The reason you might want to * convert from floating to fixed-point representations of a map is that they can yield much faster * (\~2x) remapping operations. In the converted case, `$$map_1$$` contains pairs (cvFloor(x), * cvFloor(y)) and `$$map_2$$` contains indices in a table of interpolation coefficients. * * This function cannot operate in-place. * * @param src Source image. * @param dst Destination image. It has the same size as map1 and the same type as src . * @param map1 The first map of either (x,y) points or just x values having the type CV_16SC2 , * CV_32FC1, or CV_32FC2. See #convertMaps for details on converting a floating point * representation to fixed-point for speed. * @param map2 The second map of y values having the type CV_16UC1, CV_32FC1, or none (empty map * if map1 is (x,y) points), respectively. * @param interpolation Interpolation method (see #InterpolationFlags). The methods #INTER_AREA * and #INTER_LINEAR_EXACT are not supported by this function. * @param borderMode Pixel extrapolation method (see #BorderTypes). When * borderMode=#BORDER_TRANSPARENT, it means that the pixels in the destination image that * corresponds to the "outliers" in the source image are not modified by the function. * @param borderValue Value used in case of a constant border. By default, it is 0. * NOTE: * Due to current implementation limitations the size of an input and output images should be less than 32767x32767. */ + (void)remap:(Mat*)src dst:(Mat*)dst map1:(Mat*)map1 map2:(Mat*)map2 interpolation:(int)interpolation borderMode:(BorderTypes)borderMode borderValue:(Scalar*)borderValue NS_SWIFT_NAME(remap(src:dst:map1:map2:interpolation:borderMode:borderValue:)); /** * Applies a generic geometrical transformation to an image. * * The function remap transforms the source image using the specified map: * * `$$\texttt{dst} (x,y) = \texttt{src} (map_x(x,y),map_y(x,y))$$` * * where values of pixels with non-integer coordinates are computed using one of available * interpolation methods. `$$map_x$$` and `$$map_y$$` can be encoded as separate floating-point maps * in `$$map_1$$` and `$$map_2$$` respectively, or interleaved floating-point maps of `$$(x,y)$$` in * `$$map_1$$`, or fixed-point maps created by using #convertMaps. The reason you might want to * convert from floating to fixed-point representations of a map is that they can yield much faster * (\~2x) remapping operations. In the converted case, `$$map_1$$` contains pairs (cvFloor(x), * cvFloor(y)) and `$$map_2$$` contains indices in a table of interpolation coefficients. * * This function cannot operate in-place. * * @param src Source image. * @param dst Destination image. It has the same size as map1 and the same type as src . * @param map1 The first map of either (x,y) points or just x values having the type CV_16SC2 , * CV_32FC1, or CV_32FC2. See #convertMaps for details on converting a floating point * representation to fixed-point for speed. * @param map2 The second map of y values having the type CV_16UC1, CV_32FC1, or none (empty map * if map1 is (x,y) points), respectively. * @param interpolation Interpolation method (see #InterpolationFlags). The methods #INTER_AREA * and #INTER_LINEAR_EXACT are not supported by this function. * @param borderMode Pixel extrapolation method (see #BorderTypes). When * borderMode=#BORDER_TRANSPARENT, it means that the pixels in the destination image that * corresponds to the "outliers" in the source image are not modified by the function. * NOTE: * Due to current implementation limitations the size of an input and output images should be less than 32767x32767. */ + (void)remap:(Mat*)src dst:(Mat*)dst map1:(Mat*)map1 map2:(Mat*)map2 interpolation:(int)interpolation borderMode:(BorderTypes)borderMode NS_SWIFT_NAME(remap(src:dst:map1:map2:interpolation:borderMode:)); /** * Applies a generic geometrical transformation to an image. * * The function remap transforms the source image using the specified map: * * `$$\texttt{dst} (x,y) = \texttt{src} (map_x(x,y),map_y(x,y))$$` * * where values of pixels with non-integer coordinates are computed using one of available * interpolation methods. `$$map_x$$` and `$$map_y$$` can be encoded as separate floating-point maps * in `$$map_1$$` and `$$map_2$$` respectively, or interleaved floating-point maps of `$$(x,y)$$` in * `$$map_1$$`, or fixed-point maps created by using #convertMaps. The reason you might want to * convert from floating to fixed-point representations of a map is that they can yield much faster * (\~2x) remapping operations. In the converted case, `$$map_1$$` contains pairs (cvFloor(x), * cvFloor(y)) and `$$map_2$$` contains indices in a table of interpolation coefficients. * * This function cannot operate in-place. * * @param src Source image. * @param dst Destination image. It has the same size as map1 and the same type as src . * @param map1 The first map of either (x,y) points or just x values having the type CV_16SC2 , * CV_32FC1, or CV_32FC2. See #convertMaps for details on converting a floating point * representation to fixed-point for speed. * @param map2 The second map of y values having the type CV_16UC1, CV_32FC1, or none (empty map * if map1 is (x,y) points), respectively. * @param interpolation Interpolation method (see #InterpolationFlags). The methods #INTER_AREA * and #INTER_LINEAR_EXACT are not supported by this function. * borderMode=#BORDER_TRANSPARENT, it means that the pixels in the destination image that * corresponds to the "outliers" in the source image are not modified by the function. * NOTE: * Due to current implementation limitations the size of an input and output images should be less than 32767x32767. */ + (void)remap:(Mat*)src dst:(Mat*)dst map1:(Mat*)map1 map2:(Mat*)map2 interpolation:(int)interpolation NS_SWIFT_NAME(remap(src:dst:map1:map2:interpolation:)); // // void cv::convertMaps(Mat map1, Mat map2, Mat& dstmap1, Mat& dstmap2, int dstmap1type, bool nninterpolation = false) // /** * Converts image transformation maps from one representation to another. * * The function converts a pair of maps for remap from one representation to another. The following * options ( (map1.type(), map2.type()) `$$\rightarrow$$` (dstmap1.type(), dstmap2.type()) ) are * supported: * * - `$$\texttt{(CV\_32FC1, CV\_32FC1)} \rightarrow \texttt{(CV\_16SC2, CV\_16UC1)}$$`. This is the * most frequently used conversion operation, in which the original floating-point maps (see #remap) * are converted to a more compact and much faster fixed-point representation. The first output array * contains the rounded coordinates and the second array (created only when nninterpolation=false ) * contains indices in the interpolation tables. * * - `$$\texttt{(CV\_32FC2)} \rightarrow \texttt{(CV\_16SC2, CV\_16UC1)}$$`. The same as above but * the original maps are stored in one 2-channel matrix. * * - Reverse conversion. Obviously, the reconstructed floating-point maps will not be exactly the same * as the originals. * * @param map1 The first input map of type CV_16SC2, CV_32FC1, or CV_32FC2 . * @param map2 The second input map of type CV_16UC1, CV_32FC1, or none (empty matrix), * respectively. * @param dstmap1 The first output map that has the type dstmap1type and the same size as src . * @param dstmap2 The second output map. * @param dstmap1type Type of the first output map that should be CV_16SC2, CV_32FC1, or * CV_32FC2 . * @param nninterpolation Flag indicating whether the fixed-point maps are used for the * nearest-neighbor or for a more complex interpolation. * * @see `+remap:dst:map1:map2:interpolation:borderMode:borderValue:`, `undistort`, `initUndistortRectifyMap` */ + (void)convertMaps:(Mat*)map1 map2:(Mat*)map2 dstmap1:(Mat*)dstmap1 dstmap2:(Mat*)dstmap2 dstmap1type:(int)dstmap1type nninterpolation:(BOOL)nninterpolation NS_SWIFT_NAME(convertMaps(map1:map2:dstmap1:dstmap2:dstmap1type:nninterpolation:)); /** * Converts image transformation maps from one representation to another. * * The function converts a pair of maps for remap from one representation to another. The following * options ( (map1.type(), map2.type()) `$$\rightarrow$$` (dstmap1.type(), dstmap2.type()) ) are * supported: * * - `$$\texttt{(CV\_32FC1, CV\_32FC1)} \rightarrow \texttt{(CV\_16SC2, CV\_16UC1)}$$`. This is the * most frequently used conversion operation, in which the original floating-point maps (see #remap) * are converted to a more compact and much faster fixed-point representation. The first output array * contains the rounded coordinates and the second array (created only when nninterpolation=false ) * contains indices in the interpolation tables. * * - `$$\texttt{(CV\_32FC2)} \rightarrow \texttt{(CV\_16SC2, CV\_16UC1)}$$`. The same as above but * the original maps are stored in one 2-channel matrix. * * - Reverse conversion. Obviously, the reconstructed floating-point maps will not be exactly the same * as the originals. * * @param map1 The first input map of type CV_16SC2, CV_32FC1, or CV_32FC2 . * @param map2 The second input map of type CV_16UC1, CV_32FC1, or none (empty matrix), * respectively. * @param dstmap1 The first output map that has the type dstmap1type and the same size as src . * @param dstmap2 The second output map. * @param dstmap1type Type of the first output map that should be CV_16SC2, CV_32FC1, or * CV_32FC2 . * nearest-neighbor or for a more complex interpolation. * * @see `+remap:dst:map1:map2:interpolation:borderMode:borderValue:`, `undistort`, `initUndistortRectifyMap` */ + (void)convertMaps:(Mat*)map1 map2:(Mat*)map2 dstmap1:(Mat*)dstmap1 dstmap2:(Mat*)dstmap2 dstmap1type:(int)dstmap1type NS_SWIFT_NAME(convertMaps(map1:map2:dstmap1:dstmap2:dstmap1type:)); // // Mat cv::getRotationMatrix2D(Point2f center, double angle, double scale) // /** * Calculates an affine matrix of 2D rotation. * * The function calculates the following matrix: * * `$$\begin{bmatrix} \alpha & \beta & (1- \alpha ) \cdot \texttt{center.x} - \beta \cdot \texttt{center.y} \\ - \beta & \alpha & \beta \cdot \texttt{center.x} + (1- \alpha ) \cdot \texttt{center.y} \end{bmatrix}$$` * * where * * `$$\begin{array}{l} \alpha = \texttt{scale} \cdot \cos \texttt{angle} , \\ \beta = \texttt{scale} \cdot \sin \texttt{angle} \end{array}$$` * * The transformation maps the rotation center to itself. If this is not the target, adjust the shift. * * @param center Center of the rotation in the source image. * @param angle Rotation angle in degrees. Positive values mean counter-clockwise rotation (the * coordinate origin is assumed to be the top-left corner). * @param scale Isotropic scale factor. * * @see `+getAffineTransform:dst:`, `+warpAffine:dst:M:dsize:flags:borderMode:borderValue:`, `transform` */ + (Mat*)getRotationMatrix2D:(Point2f*)center angle:(double)angle scale:(double)scale NS_SWIFT_NAME(getRotationMatrix2D(center:angle:scale:)); // // void cv::invertAffineTransform(Mat M, Mat& iM) // /** * Inverts an affine transformation. * * The function computes an inverse affine transformation represented by `$$2 \times 3$$` matrix M: * * `$$\begin{bmatrix} a_{11} & a_{12} & b_1 \\ a_{21} & a_{22} & b_2 \end{bmatrix}$$` * * The result is also a `$$2 \times 3$$` matrix of the same type as M. * * @param M Original affine transformation. * @param iM Output reverse affine transformation. */ + (void)invertAffineTransform:(Mat*)M iM:(Mat*)iM NS_SWIFT_NAME(invertAffineTransform(M:iM:)); // // Mat cv::getPerspectiveTransform(Mat src, Mat dst, int solveMethod = DECOMP_LU) // /** * Calculates a perspective transform from four pairs of the corresponding points. * * The function calculates the `$$3 \times 3$$` matrix of a perspective transform so that: * * `$$\begin{bmatrix} t_i x'_i \\ t_i y'_i \\ t_i \end{bmatrix} = \texttt{map\_matrix} \cdot \begin{bmatrix} x_i \\ y_i \\ 1 \end{bmatrix}$$` * * where * * `$$dst(i)=(x'_i,y'_i), src(i)=(x_i, y_i), i=0,1,2,3$$` * * @param src Coordinates of quadrangle vertices in the source image. * @param dst Coordinates of the corresponding quadrangle vertices in the destination image. * @param solveMethod method passed to cv::solve (#DecompTypes) * * @see `findHomography`, `+warpPerspective:dst:M:dsize:flags:borderMode:borderValue:`, `perspectiveTransform` */ + (Mat*)getPerspectiveTransform:(Mat*)src dst:(Mat*)dst solveMethod:(int)solveMethod NS_SWIFT_NAME(getPerspectiveTransform(src:dst:solveMethod:)); /** * Calculates a perspective transform from four pairs of the corresponding points. * * The function calculates the `$$3 \times 3$$` matrix of a perspective transform so that: * * `$$\begin{bmatrix} t_i x'_i \\ t_i y'_i \\ t_i \end{bmatrix} = \texttt{map\_matrix} \cdot \begin{bmatrix} x_i \\ y_i \\ 1 \end{bmatrix}$$` * * where * * `$$dst(i)=(x'_i,y'_i), src(i)=(x_i, y_i), i=0,1,2,3$$` * * @param src Coordinates of quadrangle vertices in the source image. * @param dst Coordinates of the corresponding quadrangle vertices in the destination image. * * @see `findHomography`, `+warpPerspective:dst:M:dsize:flags:borderMode:borderValue:`, `perspectiveTransform` */ + (Mat*)getPerspectiveTransform:(Mat*)src dst:(Mat*)dst NS_SWIFT_NAME(getPerspectiveTransform(src:dst:)); // // Mat cv::getAffineTransform(vector_Point2f src, vector_Point2f dst) // + (Mat*)getAffineTransform:(NSArray*)src dst:(NSArray*)dst NS_SWIFT_NAME(getAffineTransform(src:dst:)); // // void cv::getRectSubPix(Mat image, Size patchSize, Point2f center, Mat& patch, int patchType = -1) // /** * Retrieves a pixel rectangle from an image with sub-pixel accuracy. * * The function getRectSubPix extracts pixels from src: * * `$$patch(x, y) = src(x + \texttt{center.x} - ( \texttt{dst.cols} -1)*0.5, y + \texttt{center.y} - ( \texttt{dst.rows} -1)*0.5)$$` * * where the values of the pixels at non-integer coordinates are retrieved using bilinear * interpolation. Every channel of multi-channel images is processed independently. Also * the image should be a single channel or three channel image. While the center of the * rectangle must be inside the image, parts of the rectangle may be outside. * * @param image Source image. * @param patchSize Size of the extracted patch. * @param center Floating point coordinates of the center of the extracted rectangle within the * source image. The center must be inside the image. * @param patch Extracted patch that has the size patchSize and the same number of channels as src . * @param patchType Depth of the extracted pixels. By default, they have the same depth as src . * * @see `+warpAffine:dst:M:dsize:flags:borderMode:borderValue:`, `+warpPerspective:dst:M:dsize:flags:borderMode:borderValue:` */ + (void)getRectSubPix:(Mat*)image patchSize:(Size2i*)patchSize center:(Point2f*)center patch:(Mat*)patch patchType:(int)patchType NS_SWIFT_NAME(getRectSubPix(image:patchSize:center:patch:patchType:)); /** * Retrieves a pixel rectangle from an image with sub-pixel accuracy. * * The function getRectSubPix extracts pixels from src: * * `$$patch(x, y) = src(x + \texttt{center.x} - ( \texttt{dst.cols} -1)*0.5, y + \texttt{center.y} - ( \texttt{dst.rows} -1)*0.5)$$` * * where the values of the pixels at non-integer coordinates are retrieved using bilinear * interpolation. Every channel of multi-channel images is processed independently. Also * the image should be a single channel or three channel image. While the center of the * rectangle must be inside the image, parts of the rectangle may be outside. * * @param image Source image. * @param patchSize Size of the extracted patch. * @param center Floating point coordinates of the center of the extracted rectangle within the * source image. The center must be inside the image. * @param patch Extracted patch that has the size patchSize and the same number of channels as src . * * @see `+warpAffine:dst:M:dsize:flags:borderMode:borderValue:`, `+warpPerspective:dst:M:dsize:flags:borderMode:borderValue:` */ + (void)getRectSubPix:(Mat*)image patchSize:(Size2i*)patchSize center:(Point2f*)center patch:(Mat*)patch NS_SWIFT_NAME(getRectSubPix(image:patchSize:center:patch:)); // // void cv::logPolar(Mat src, Mat& dst, Point2f center, double M, int flags) // /** * Remaps an image to semilog-polar coordinates space. * * @deprecated This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags+WARP_POLAR_LOG); * * * Transform the source image using the following transformation (See REF: polar_remaps_reference_image "Polar remaps reference image d)"): * `$$\begin{array}{l} * dst( \rho , \phi ) = src(x,y) \\ * dst.size() \leftarrow src.size() * \end{array}$$` * * where * `$$\begin{array}{l} * I = (dx,dy) = (x - center.x,y - center.y) \\ * \rho = M \cdot log_e(\texttt{magnitude} (I)) ,\\ * \phi = Kangle \cdot \texttt{angle} (I) \\ * \end{array}$$` * * and * `$$\begin{array}{l} * M = src.cols / log_e(maxRadius) \\ * Kangle = src.rows / 2\Pi \\ * \end{array}$$` * * The function emulates the human "foveal" vision and can be used for fast scale and * rotation-invariant template matching, for object tracking and so forth. * @param src Source image * @param dst Destination image. It will have same size and type as src. * @param center The transformation center; where the output precision is maximal * @param M Magnitude scale parameter. It determines the radius of the bounding circle to transform too. * @param flags A combination of interpolation methods, see #InterpolationFlags * * NOTE: * - The function can not operate in-place. * - To calculate magnitude and angle in degrees #cartToPolar is used internally thus angles are measured from 0 to 360 with accuracy about 0.3 degrees. * * @see `cv::linearPolar` */ + (void)logPolar:(Mat*)src dst:(Mat*)dst center:(Point2f*)center M:(double)M flags:(int)flags NS_SWIFT_NAME(logPolar(src:dst:center:M:flags:)) DEPRECATED_ATTRIBUTE; // // void cv::linearPolar(Mat src, Mat& dst, Point2f center, double maxRadius, int flags) // /** * Remaps an image to polar coordinates space. * * @deprecated This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags) * * * Transform the source image using the following transformation (See REF: polar_remaps_reference_image "Polar remaps reference image c)"): * `$$\begin{array}{l} * dst( \rho , \phi ) = src(x,y) \\ * dst.size() \leftarrow src.size() * \end{array}$$` * * where * `$$\begin{array}{l} * I = (dx,dy) = (x - center.x,y - center.y) \\ * \rho = Kmag \cdot \texttt{magnitude} (I) ,\\ * \phi = angle \cdot \texttt{angle} (I) * \end{array}$$` * * and * `$$\begin{array}{l} * Kx = src.cols / maxRadius \\ * Ky = src.rows / 2\Pi * \end{array}$$` * * * @param src Source image * @param dst Destination image. It will have same size and type as src. * @param center The transformation center; * @param maxRadius The radius of the bounding circle to transform. It determines the inverse magnitude scale parameter too. * @param flags A combination of interpolation methods, see #InterpolationFlags * * NOTE: * - The function can not operate in-place. * - To calculate magnitude and angle in degrees #cartToPolar is used internally thus angles are measured from 0 to 360 with accuracy about 0.3 degrees. * * @see `cv::logPolar` */ + (void)linearPolar:(Mat*)src dst:(Mat*)dst center:(Point2f*)center maxRadius:(double)maxRadius flags:(int)flags NS_SWIFT_NAME(linearPolar(src:dst:center:maxRadius:flags:)) DEPRECATED_ATTRIBUTE; // // void cv::warpPolar(Mat src, Mat& dst, Size dsize, Point2f center, double maxRadius, int flags) // /** * Remaps an image to polar or semilog-polar coordinates space * * polar_remaps_reference_image * ![Polar remaps reference](pics/polar_remap_doc.png) * * Transform the source image using the following transformation: * `$$ * dst(\rho , \phi ) = src(x,y) * $$` * * where * `$$ * \begin{array}{l} * \vec{I} = (x - center.x, \;y - center.y) \\ * \phi = Kangle \cdot \texttt{angle} (\vec{I}) \\ * \rho = \left\{\begin{matrix} * Klin \cdot \texttt{magnitude} (\vec{I}) & default \\ * Klog \cdot log_e(\texttt{magnitude} (\vec{I})) & if \; semilog \\ * \end{matrix}\right. * \end{array} * $$` * * and * `$$ * \begin{array}{l} * Kangle = dsize.height / 2\Pi \\ * Klin = dsize.width / maxRadius \\ * Klog = dsize.width / log_e(maxRadius) \\ * \end{array} * $$` * * * \par Linear vs semilog mapping * * Polar mapping can be linear or semi-log. Add one of #WarpPolarMode to `flags` to specify the polar mapping mode. * * Linear is the default mode. * * The semilog mapping emulates the human "foveal" vision that permit very high acuity on the line of sight (central vision) * in contrast to peripheral vision where acuity is minor. * * \par Option on `dsize`: * * - if both values in `dsize <=0 ` (default), * the destination image will have (almost) same area of source bounding circle: * `$$\begin{array}{l} * dsize.area \leftarrow (maxRadius^2 \cdot \Pi) \\ * dsize.width = \texttt{cvRound}(maxRadius) \\ * dsize.height = \texttt{cvRound}(maxRadius \cdot \Pi) \\ * \end{array}$$` * * * - if only `dsize.height <= 0`, * the destination image area will be proportional to the bounding circle area but scaled by `Kx * Kx`: * `$$\begin{array}{l} * dsize.height = \texttt{cvRound}(dsize.width \cdot \Pi) \\ * \end{array} * $$` * * - if both values in `dsize > 0 `, * the destination image will have the given size therefore the area of the bounding circle will be scaled to `dsize`. * * * \par Reverse mapping * * You can get reverse mapping adding #WARP_INVERSE_MAP to `flags` * \snippet polar_transforms.cpp InverseMap * * In addiction, to calculate the original coordinate from a polar mapped coordinate `$$(rho, phi)->(x, y)$$`: * \snippet polar_transforms.cpp InverseCoordinate * * @param src Source image. * @param dst Destination image. It will have same type as src. * @param dsize The destination image size (see description for valid options). * @param center The transformation center. * @param maxRadius The radius of the bounding circle to transform. It determines the inverse magnitude scale parameter too. * @param flags A combination of interpolation methods, #InterpolationFlags + #WarpPolarMode. * - Add #WARP_POLAR_LINEAR to select linear polar mapping (default) * - Add #WARP_POLAR_LOG to select semilog polar mapping * - Add #WARP_INVERSE_MAP for reverse mapping. * NOTE: * - The function can not operate in-place. * - To calculate magnitude and angle in degrees #cartToPolar is used internally thus angles are measured from 0 to 360 with accuracy about 0.3 degrees. * - This function uses #remap. Due to current implementation limitations the size of an input and output images should be less than 32767x32767. * * @see `cv::remap` */ + (void)warpPolar:(Mat*)src dst:(Mat*)dst dsize:(Size2i*)dsize center:(Point2f*)center maxRadius:(double)maxRadius flags:(int)flags NS_SWIFT_NAME(warpPolar(src:dst:dsize:center:maxRadius:flags:)); // // void cv::integral(Mat src, Mat& sum, Mat& sqsum, Mat& tilted, int sdepth = -1, int sqdepth = -1) // /** * Calculates the integral of an image. * * The function calculates one or more integral images for the source image as follows: * * `$$\texttt{sum} (X,Y) = \sum _{x * * Calculates the cross-power spectrum of two supplied source arrays. The arrays are padded if needed * with getOptimalDFTSize. * * The function performs the following equations: * - First it applies a Hanning window (see ) to each * image to remove possible edge effects. This window is cached until the array size changes to speed * up processing time. * - Next it computes the forward DFTs of each source array: * `$$\mathbf{G}_a = \mathcal{F}\{src_1\}, \; \mathbf{G}_b = \mathcal{F}\{src_2\}$$` * where `$$\mathcal{F}$$` is the forward DFT. * - It then computes the cross-power spectrum of each frequency domain array: * `$$R = \frac{ \mathbf{G}_a \mathbf{G}_b^*}{|\mathbf{G}_a \mathbf{G}_b^*|}$$` * - Next the cross-correlation is converted back into the time domain via the inverse DFT: * `$$r = \mathcal{F}^{-1}\{R\}$$` * - Finally, it computes the peak location and computes a 5x5 weighted centroid around the peak to * achieve sub-pixel accuracy. * `$$(\Delta x, \Delta y) = \texttt{weightedCentroid} \{\arg \max_{(x, y)}\{r\}\}$$` * - If non-zero, the response parameter is computed as the sum of the elements of r within the 5x5 * centroid around the peak location. It is normalized to a maximum of 1 (meaning there is a single * peak) and will be smaller when there are multiple peaks. * * @param src1 Source floating point array (CV_32FC1 or CV_64FC1) * @param src2 Source floating point array (CV_32FC1 or CV_64FC1) * @param window Floating point array with windowing coefficients to reduce edge effects (optional). * @param response Signal power within the 5x5 centroid around the peak, between 0 and 1 (optional). * @return detected phase shift (sub-pixel) between the two arrays. * * @see `dft`, `getOptimalDFTSize`, `idft`, `mulSpectrums createHanningWindow` */ + (Point2d*)phaseCorrelate:(Mat*)src1 src2:(Mat*)src2 window:(Mat*)window response:(double*)response NS_SWIFT_NAME(phaseCorrelate(src1:src2:window:response:)); /** * The function is used to detect translational shifts that occur between two images. * * The operation takes advantage of the Fourier shift theorem for detecting the translational shift in * the frequency domain. It can be used for fast image registration as well as motion estimation. For * more information please see * * Calculates the cross-power spectrum of two supplied source arrays. The arrays are padded if needed * with getOptimalDFTSize. * * The function performs the following equations: * - First it applies a Hanning window (see ) to each * image to remove possible edge effects. This window is cached until the array size changes to speed * up processing time. * - Next it computes the forward DFTs of each source array: * `$$\mathbf{G}_a = \mathcal{F}\{src_1\}, \; \mathbf{G}_b = \mathcal{F}\{src_2\}$$` * where `$$\mathcal{F}$$` is the forward DFT. * - It then computes the cross-power spectrum of each frequency domain array: * `$$R = \frac{ \mathbf{G}_a \mathbf{G}_b^*}{|\mathbf{G}_a \mathbf{G}_b^*|}$$` * - Next the cross-correlation is converted back into the time domain via the inverse DFT: * `$$r = \mathcal{F}^{-1}\{R\}$$` * - Finally, it computes the peak location and computes a 5x5 weighted centroid around the peak to * achieve sub-pixel accuracy. * `$$(\Delta x, \Delta y) = \texttt{weightedCentroid} \{\arg \max_{(x, y)}\{r\}\}$$` * - If non-zero, the response parameter is computed as the sum of the elements of r within the 5x5 * centroid around the peak location. It is normalized to a maximum of 1 (meaning there is a single * peak) and will be smaller when there are multiple peaks. * * @param src1 Source floating point array (CV_32FC1 or CV_64FC1) * @param src2 Source floating point array (CV_32FC1 or CV_64FC1) * @param window Floating point array with windowing coefficients to reduce edge effects (optional). * @return detected phase shift (sub-pixel) between the two arrays. * * @see `dft`, `getOptimalDFTSize`, `idft`, `mulSpectrums createHanningWindow` */ + (Point2d*)phaseCorrelate:(Mat*)src1 src2:(Mat*)src2 window:(Mat*)window NS_SWIFT_NAME(phaseCorrelate(src1:src2:window:)); /** * The function is used to detect translational shifts that occur between two images. * * The operation takes advantage of the Fourier shift theorem for detecting the translational shift in * the frequency domain. It can be used for fast image registration as well as motion estimation. For * more information please see * * Calculates the cross-power spectrum of two supplied source arrays. The arrays are padded if needed * with getOptimalDFTSize. * * The function performs the following equations: * - First it applies a Hanning window (see ) to each * image to remove possible edge effects. This window is cached until the array size changes to speed * up processing time. * - Next it computes the forward DFTs of each source array: * `$$\mathbf{G}_a = \mathcal{F}\{src_1\}, \; \mathbf{G}_b = \mathcal{F}\{src_2\}$$` * where `$$\mathcal{F}$$` is the forward DFT. * - It then computes the cross-power spectrum of each frequency domain array: * `$$R = \frac{ \mathbf{G}_a \mathbf{G}_b^*}{|\mathbf{G}_a \mathbf{G}_b^*|}$$` * - Next the cross-correlation is converted back into the time domain via the inverse DFT: * `$$r = \mathcal{F}^{-1}\{R\}$$` * - Finally, it computes the peak location and computes a 5x5 weighted centroid around the peak to * achieve sub-pixel accuracy. * `$$(\Delta x, \Delta y) = \texttt{weightedCentroid} \{\arg \max_{(x, y)}\{r\}\}$$` * - If non-zero, the response parameter is computed as the sum of the elements of r within the 5x5 * centroid around the peak location. It is normalized to a maximum of 1 (meaning there is a single * peak) and will be smaller when there are multiple peaks. * * @param src1 Source floating point array (CV_32FC1 or CV_64FC1) * @param src2 Source floating point array (CV_32FC1 or CV_64FC1) * @return detected phase shift (sub-pixel) between the two arrays. * * @see `dft`, `getOptimalDFTSize`, `idft`, `mulSpectrums createHanningWindow` */ + (Point2d*)phaseCorrelate:(Mat*)src1 src2:(Mat*)src2 NS_SWIFT_NAME(phaseCorrelate(src1:src2:)); // // void cv::createHanningWindow(Mat& dst, Size winSize, int type) // /** * This function computes a Hanning window coefficients in two dimensions. * * See (http://en.wikipedia.org/wiki/Hann_function) and (http://en.wikipedia.org/wiki/Window_function) * for more information. * * An example is shown below: * * // create hanning window of size 100x100 and type CV_32F * Mat hann; * createHanningWindow(hann, Size(100, 100), CV_32F); * * @param dst Destination array to place Hann coefficients in * @param winSize The window size specifications (both width and height must be > 1) * @param type Created array type */ + (void)createHanningWindow:(Mat*)dst winSize:(Size2i*)winSize type:(int)type NS_SWIFT_NAME(createHanningWindow(dst:winSize:type:)); // // void cv::divSpectrums(Mat a, Mat b, Mat& c, int flags, bool conjB = false) // /** * Performs the per-element division of the first Fourier spectrum by the second Fourier spectrum. * * The function cv::divSpectrums performs the per-element division of the first array by the second array. * The arrays are CCS-packed or complex matrices that are results of a real or complex Fourier transform. * * @param a first input array. * @param b second input array of the same size and type as src1 . * @param c output array of the same size and type as src1 . * @param flags operation flags; currently, the only supported flag is cv::DFT_ROWS, which indicates that * each row of src1 and src2 is an independent 1D Fourier spectrum. If you do not want to use this flag, then simply add a `0` as value. * @param conjB optional flag that conjugates the second input array before the multiplication (true) * or not (false). */ + (void)divSpectrums:(Mat*)a b:(Mat*)b c:(Mat*)c flags:(int)flags conjB:(BOOL)conjB NS_SWIFT_NAME(divSpectrums(a:b:c:flags:conjB:)); /** * Performs the per-element division of the first Fourier spectrum by the second Fourier spectrum. * * The function cv::divSpectrums performs the per-element division of the first array by the second array. * The arrays are CCS-packed or complex matrices that are results of a real or complex Fourier transform. * * @param a first input array. * @param b second input array of the same size and type as src1 . * @param c output array of the same size and type as src1 . * @param flags operation flags; currently, the only supported flag is cv::DFT_ROWS, which indicates that * each row of src1 and src2 is an independent 1D Fourier spectrum. If you do not want to use this flag, then simply add a `0` as value. * or not (false). */ + (void)divSpectrums:(Mat*)a b:(Mat*)b c:(Mat*)c flags:(int)flags NS_SWIFT_NAME(divSpectrums(a:b:c:flags:)); // // double cv::threshold(Mat src, Mat& dst, double thresh, double maxval, ThresholdTypes type) // /** * Applies a fixed-level threshold to each array element. * * The function applies fixed-level thresholding to a multiple-channel array. The function is typically * used to get a bi-level (binary) image out of a grayscale image ( #compare could be also used for * this purpose) or for removing a noise, that is, filtering out pixels with too small or too large * values. There are several types of thresholding supported by the function. They are determined by * type parameter. * * Also, the special values #THRESH_OTSU or #THRESH_TRIANGLE may be combined with one of the * above values. In these cases, the function determines the optimal threshold value using the Otsu's * or Triangle algorithm and uses it instead of the specified thresh. * * NOTE: Currently, the Otsu's and Triangle methods are implemented only for 8-bit single-channel images. * * @param src input array (multiple-channel, 8-bit or 32-bit floating point). * @param dst output array of the same size and type and the same number of channels as src. * @param thresh threshold value. * @param maxval maximum value to use with the #THRESH_BINARY and #THRESH_BINARY_INV thresholding * types. * @param type thresholding type (see #ThresholdTypes). * @return the computed threshold value if Otsu's or Triangle methods used. * * @see `+adaptiveThreshold:dst:maxValue:adaptiveMethod:thresholdType:blockSize:C:`, `+findContours:contours:hierarchy:mode:method:offset:`, `compare`, `min`, `max` */ + (double)threshold:(Mat*)src dst:(Mat*)dst thresh:(double)thresh maxval:(double)maxval type:(ThresholdTypes)type NS_SWIFT_NAME(threshold(src:dst:thresh:maxval:type:)); // // void cv::adaptiveThreshold(Mat src, Mat& dst, double maxValue, AdaptiveThresholdTypes adaptiveMethod, ThresholdTypes thresholdType, int blockSize, double C) // /** * Applies an adaptive threshold to an array. * * The function transforms a grayscale image to a binary image according to the formulae: * - **THRESH_BINARY** * `$$\newcommand{\fork}[4]{ \left\{ \begin{array}{l l} #1 & \text{#2}\\\\ #3 & \text{#4}\\\\ \end{array} \right.} dst(x,y) = \fork{\texttt{maxValue}}{if \(src(x,y) > T(x,y)\)}{0}{otherwise}$$` * - **THRESH_BINARY_INV** * `$$\newcommand{\fork}[4]{ \left\{ \begin{array}{l l} #1 & \text{#2}\\\\ #3 & \text{#4}\\\\ \end{array} \right.} dst(x,y) = \fork{0}{if \(src(x,y) > T(x,y)\)}{\texttt{maxValue}}{otherwise}$$` * where `$$T(x,y)$$` is a threshold calculated individually for each pixel (see adaptiveMethod parameter). * * The function can process the image in-place. * * @param src Source 8-bit single-channel image. * @param dst Destination image of the same size and the same type as src. * @param maxValue Non-zero value assigned to the pixels for which the condition is satisfied * @param adaptiveMethod Adaptive thresholding algorithm to use, see #AdaptiveThresholdTypes. * The #BORDER_REPLICATE | #BORDER_ISOLATED is used to process boundaries. * @param thresholdType Thresholding type that must be either #THRESH_BINARY or #THRESH_BINARY_INV, * see #ThresholdTypes. * @param blockSize Size of a pixel neighborhood that is used to calculate a threshold value for the * pixel: 3, 5, 7, and so on. * @param C Constant subtracted from the mean or weighted mean (see the details below). Normally, it * is positive but may be zero or negative as well. * * @see `+threshold:dst:thresh:maxval:type:`, `+blur:dst:ksize:anchor:borderType:`, `+GaussianBlur:dst:ksize:sigmaX:sigmaY:borderType:` */ + (void)adaptiveThreshold:(Mat*)src dst:(Mat*)dst maxValue:(double)maxValue adaptiveMethod:(AdaptiveThresholdTypes)adaptiveMethod thresholdType:(ThresholdTypes)thresholdType blockSize:(int)blockSize C:(double)C NS_SWIFT_NAME(adaptiveThreshold(src:dst:maxValue:adaptiveMethod:thresholdType:blockSize:C:)); // // void cv::pyrDown(Mat src, Mat& dst, Size dstsize = Size(), BorderTypes borderType = BORDER_DEFAULT) // /** * Blurs an image and downsamples it. * * By default, size of the output image is computed as `Size((src.cols+1)/2, (src.rows+1)/2)`, but in * any case, the following conditions should be satisfied: * * `$$\begin{array}{l} | \texttt{dstsize.width} *2-src.cols| \leq 2 \\ | \texttt{dstsize.height} *2-src.rows| \leq 2 \end{array}$$` * * The function performs the downsampling step of the Gaussian pyramid construction. First, it * convolves the source image with the kernel: * * `$$\frac{1}{256} \begin{bmatrix} 1 & 4 & 6 & 4 & 1 \\ 4 & 16 & 24 & 16 & 4 \\ 6 & 24 & 36 & 24 & 6 \\ 4 & 16 & 24 & 16 & 4 \\ 1 & 4 & 6 & 4 & 1 \end{bmatrix}$$` * * Then, it downsamples the image by rejecting even rows and columns. * * @param src input image. * @param dst output image; it has the specified size and the same type as src. * @param dstsize size of the output image. * @param borderType Pixel extrapolation method, see #BorderTypes (#BORDER_CONSTANT isn't supported) */ + (void)pyrDown:(Mat*)src dst:(Mat*)dst dstsize:(Size2i*)dstsize borderType:(BorderTypes)borderType NS_SWIFT_NAME(pyrDown(src:dst:dstsize:borderType:)); /** * Blurs an image and downsamples it. * * By default, size of the output image is computed as `Size((src.cols+1)/2, (src.rows+1)/2)`, but in * any case, the following conditions should be satisfied: * * `$$\begin{array}{l} | \texttt{dstsize.width} *2-src.cols| \leq 2 \\ | \texttt{dstsize.height} *2-src.rows| \leq 2 \end{array}$$` * * The function performs the downsampling step of the Gaussian pyramid construction. First, it * convolves the source image with the kernel: * * `$$\frac{1}{256} \begin{bmatrix} 1 & 4 & 6 & 4 & 1 \\ 4 & 16 & 24 & 16 & 4 \\ 6 & 24 & 36 & 24 & 6 \\ 4 & 16 & 24 & 16 & 4 \\ 1 & 4 & 6 & 4 & 1 \end{bmatrix}$$` * * Then, it downsamples the image by rejecting even rows and columns. * * @param src input image. * @param dst output image; it has the specified size and the same type as src. * @param dstsize size of the output image. */ + (void)pyrDown:(Mat*)src dst:(Mat*)dst dstsize:(Size2i*)dstsize NS_SWIFT_NAME(pyrDown(src:dst:dstsize:)); /** * Blurs an image and downsamples it. * * By default, size of the output image is computed as `Size((src.cols+1)/2, (src.rows+1)/2)`, but in * any case, the following conditions should be satisfied: * * `$$\begin{array}{l} | \texttt{dstsize.width} *2-src.cols| \leq 2 \\ | \texttt{dstsize.height} *2-src.rows| \leq 2 \end{array}$$` * * The function performs the downsampling step of the Gaussian pyramid construction. First, it * convolves the source image with the kernel: * * `$$\frac{1}{256} \begin{bmatrix} 1 & 4 & 6 & 4 & 1 \\ 4 & 16 & 24 & 16 & 4 \\ 6 & 24 & 36 & 24 & 6 \\ 4 & 16 & 24 & 16 & 4 \\ 1 & 4 & 6 & 4 & 1 \end{bmatrix}$$` * * Then, it downsamples the image by rejecting even rows and columns. * * @param src input image. * @param dst output image; it has the specified size and the same type as src. */ + (void)pyrDown:(Mat*)src dst:(Mat*)dst NS_SWIFT_NAME(pyrDown(src:dst:)); // // void cv::pyrUp(Mat src, Mat& dst, Size dstsize = Size(), BorderTypes borderType = BORDER_DEFAULT) // /** * Upsamples an image and then blurs it. * * By default, size of the output image is computed as `Size(src.cols\*2, (src.rows\*2)`, but in any * case, the following conditions should be satisfied: * * `$$\begin{array}{l} | \texttt{dstsize.width} -src.cols*2| \leq ( \texttt{dstsize.width} \mod 2) \\ | \texttt{dstsize.height} -src.rows*2| \leq ( \texttt{dstsize.height} \mod 2) \end{array}$$` * * The function performs the upsampling step of the Gaussian pyramid construction, though it can * actually be used to construct the Laplacian pyramid. First, it upsamples the source image by * injecting even zero rows and columns and then convolves the result with the same kernel as in * pyrDown multiplied by 4. * * @param src input image. * @param dst output image. It has the specified size and the same type as src . * @param dstsize size of the output image. * @param borderType Pixel extrapolation method, see #BorderTypes (only #BORDER_DEFAULT is supported) */ + (void)pyrUp:(Mat*)src dst:(Mat*)dst dstsize:(Size2i*)dstsize borderType:(BorderTypes)borderType NS_SWIFT_NAME(pyrUp(src:dst:dstsize:borderType:)); /** * Upsamples an image and then blurs it. * * By default, size of the output image is computed as `Size(src.cols\*2, (src.rows\*2)`, but in any * case, the following conditions should be satisfied: * * `$$\begin{array}{l} | \texttt{dstsize.width} -src.cols*2| \leq ( \texttt{dstsize.width} \mod 2) \\ | \texttt{dstsize.height} -src.rows*2| \leq ( \texttt{dstsize.height} \mod 2) \end{array}$$` * * The function performs the upsampling step of the Gaussian pyramid construction, though it can * actually be used to construct the Laplacian pyramid. First, it upsamples the source image by * injecting even zero rows and columns and then convolves the result with the same kernel as in * pyrDown multiplied by 4. * * @param src input image. * @param dst output image. It has the specified size and the same type as src . * @param dstsize size of the output image. */ + (void)pyrUp:(Mat*)src dst:(Mat*)dst dstsize:(Size2i*)dstsize NS_SWIFT_NAME(pyrUp(src:dst:dstsize:)); /** * Upsamples an image and then blurs it. * * By default, size of the output image is computed as `Size(src.cols\*2, (src.rows\*2)`, but in any * case, the following conditions should be satisfied: * * `$$\begin{array}{l} | \texttt{dstsize.width} -src.cols*2| \leq ( \texttt{dstsize.width} \mod 2) \\ | \texttt{dstsize.height} -src.rows*2| \leq ( \texttt{dstsize.height} \mod 2) \end{array}$$` * * The function performs the upsampling step of the Gaussian pyramid construction, though it can * actually be used to construct the Laplacian pyramid. First, it upsamples the source image by * injecting even zero rows and columns and then convolves the result with the same kernel as in * pyrDown multiplied by 4. * * @param src input image. * @param dst output image. It has the specified size and the same type as src . */ + (void)pyrUp:(Mat*)src dst:(Mat*)dst NS_SWIFT_NAME(pyrUp(src:dst:)); // // void cv::calcHist(vector_Mat images, vector_int channels, Mat mask, Mat& hist, vector_int histSize, vector_float ranges, bool accumulate = false) // + (void)calcHist:(NSArray*)images channels:(IntVector*)channels mask:(Mat*)mask hist:(Mat*)hist histSize:(IntVector*)histSize ranges:(FloatVector*)ranges accumulate:(BOOL)accumulate NS_SWIFT_NAME(calcHist(images:channels:mask:hist:histSize:ranges:accumulate:)); + (void)calcHist:(NSArray*)images channels:(IntVector*)channels mask:(Mat*)mask hist:(Mat*)hist histSize:(IntVector*)histSize ranges:(FloatVector*)ranges NS_SWIFT_NAME(calcHist(images:channels:mask:hist:histSize:ranges:)); // // void cv::calcBackProject(vector_Mat images, vector_int channels, Mat hist, Mat& dst, vector_float ranges, double scale) // + (void)calcBackProject:(NSArray*)images channels:(IntVector*)channels hist:(Mat*)hist dst:(Mat*)dst ranges:(FloatVector*)ranges scale:(double)scale NS_SWIFT_NAME(calcBackProject(images:channels:hist:dst:ranges:scale:)); // // double cv::compareHist(Mat H1, Mat H2, HistCompMethods method) // /** * Compares two histograms. * * The function cv::compareHist compares two dense or two sparse histograms using the specified method. * * The function returns `$$d(H_1, H_2)$$` . * * While the function works well with 1-, 2-, 3-dimensional dense histograms, it may not be suitable * for high-dimensional sparse histograms. In such histograms, because of aliasing and sampling * problems, the coordinates of non-zero histogram bins can slightly shift. To compare such histograms * or more general sparse configurations of weighted points, consider using the #EMD function. * * @param H1 First compared histogram. * @param H2 Second compared histogram of the same size as H1 . * @param method Comparison method, see #HistCompMethods */ + (double)compareHist:(Mat*)H1 H2:(Mat*)H2 method:(HistCompMethods)method NS_SWIFT_NAME(compareHist(H1:H2:method:)); // // void cv::equalizeHist(Mat src, Mat& dst) // /** * Equalizes the histogram of a grayscale image. * * The function equalizes the histogram of the input image using the following algorithm: * * - Calculate the histogram `$$H$$` for src . * - Normalize the histogram so that the sum of histogram bins is 255. * - Compute the integral of the histogram: * `$$H'_i = \sum _{0 \le j < i} H(j)$$` * - Transform the image using `$$H'$$` as a look-up table: `$$\texttt{dst}(x,y) = H'(\texttt{src}(x,y))$$` * * The algorithm normalizes the brightness and increases the contrast of the image. * * @param src Source 8-bit single channel image. * @param dst Destination image of the same size and type as src . */ + (void)equalizeHist:(Mat*)src dst:(Mat*)dst NS_SWIFT_NAME(equalizeHist(src:dst:)); // // Ptr_CLAHE cv::createCLAHE(double clipLimit = 40.0, Size tileGridSize = Size(8, 8)) // /** * Creates a smart pointer to a cv::CLAHE class and initializes it. * * @param clipLimit Threshold for contrast limiting. * @param tileGridSize Size of grid for histogram equalization. Input image will be divided into * equally sized rectangular tiles. tileGridSize defines the number of tiles in row and column. */ + (CLAHE*)createCLAHE:(double)clipLimit tileGridSize:(Size2i*)tileGridSize NS_SWIFT_NAME(createCLAHE(clipLimit:tileGridSize:)); /** * Creates a smart pointer to a cv::CLAHE class and initializes it. * * @param clipLimit Threshold for contrast limiting. * equally sized rectangular tiles. tileGridSize defines the number of tiles in row and column. */ + (CLAHE*)createCLAHE:(double)clipLimit NS_SWIFT_NAME(createCLAHE(clipLimit:)); /** * Creates a smart pointer to a cv::CLAHE class and initializes it. * * equally sized rectangular tiles. tileGridSize defines the number of tiles in row and column. */ + (CLAHE*)createCLAHE NS_SWIFT_NAME(createCLAHE()); // // float cv::wrapperEMD(Mat signature1, Mat signature2, DistanceTypes distType, Mat cost = Mat(), _hidden_ & lowerBound = cv::Ptr(), Mat& flow = Mat()) // /** * Computes the "minimal work" distance between two weighted point configurations. * * The function computes the earth mover distance and/or a lower boundary of the distance between the * two weighted point configurations. One of the applications described in CITE: RubnerSept98, * CITE: Rubner2000 is multi-dimensional histogram comparison for image retrieval. EMD is a transportation * problem that is solved using some modification of a simplex algorithm, thus the complexity is * exponential in the worst case, though, on average it is much faster. In the case of a real metric * the lower boundary can be calculated even faster (using linear-time algorithm) and it can be used * to determine roughly whether the two signatures are far enough so that they cannot relate to the * same object. * * @param signature1 First signature, a `$$\texttt{size1}\times \texttt{dims}+1$$` floating-point matrix. * Each row stores the point weight followed by the point coordinates. The matrix is allowed to have * a single column (weights only) if the user-defined cost matrix is used. The weights must be * non-negative and have at least one non-zero value. * @param signature2 Second signature of the same format as signature1 , though the number of rows * may be different. The total weights may be different. In this case an extra "dummy" point is added * to either signature1 or signature2. The weights must be non-negative and have at least one non-zero * value. * @param distType Used metric. See #DistanceTypes. * @param cost User-defined `$$\texttt{size1}\times \texttt{size2}$$` cost matrix. Also, if a cost matrix * is used, lower boundary lowerBound cannot be calculated because it needs a metric function. * @param lowerBound Optional input/output parameter: lower boundary of a distance between the two * signatures that is a distance between mass centers. The lower boundary may not be calculated if * the user-defined cost matrix is used, the total weights of point configurations are not equal, or * if the signatures consist of weights only (the signature matrices have a single column). You * *must** initialize \*lowerBound . If the calculated distance between mass centers is greater or * equal to \*lowerBound (it means that the signatures are far enough), the function does not * calculate EMD. In any case \*lowerBound is set to the calculated distance between mass centers on * return. Thus, if you want to calculate both distance between mass centers and EMD, \*lowerBound * should be set to 0. * @param flow Resultant `$$\texttt{size1} \times \texttt{size2}$$` flow matrix: `$$\texttt{flow}_{i,j}$$` is * a flow from `$$i$$` -th point of signature1 to `$$j$$` -th point of signature2 . */ + (float)EMD:(Mat*)signature1 signature2:(Mat*)signature2 distType:(DistanceTypes)distType cost:(Mat*)cost flow:(Mat*)flow NS_SWIFT_NAME(wrapperEMD(signature1:signature2:distType:cost:flow:)); /** * Computes the "minimal work" distance between two weighted point configurations. * * The function computes the earth mover distance and/or a lower boundary of the distance between the * two weighted point configurations. One of the applications described in CITE: RubnerSept98, * CITE: Rubner2000 is multi-dimensional histogram comparison for image retrieval. EMD is a transportation * problem that is solved using some modification of a simplex algorithm, thus the complexity is * exponential in the worst case, though, on average it is much faster. In the case of a real metric * the lower boundary can be calculated even faster (using linear-time algorithm) and it can be used * to determine roughly whether the two signatures are far enough so that they cannot relate to the * same object. * * @param signature1 First signature, a `$$\texttt{size1}\times \texttt{dims}+1$$` floating-point matrix. * Each row stores the point weight followed by the point coordinates. The matrix is allowed to have * a single column (weights only) if the user-defined cost matrix is used. The weights must be * non-negative and have at least one non-zero value. * @param signature2 Second signature of the same format as signature1 , though the number of rows * may be different. The total weights may be different. In this case an extra "dummy" point is added * to either signature1 or signature2. The weights must be non-negative and have at least one non-zero * value. * @param distType Used metric. See #DistanceTypes. * @param cost User-defined `$$\texttt{size1}\times \texttt{size2}$$` cost matrix. Also, if a cost matrix * is used, lower boundary lowerBound cannot be calculated because it needs a metric function. * @param lowerBound Optional input/output parameter: lower boundary of a distance between the two * signatures that is a distance between mass centers. The lower boundary may not be calculated if * the user-defined cost matrix is used, the total weights of point configurations are not equal, or * if the signatures consist of weights only (the signature matrices have a single column). You * *must** initialize \*lowerBound . If the calculated distance between mass centers is greater or * equal to \*lowerBound (it means that the signatures are far enough), the function does not * calculate EMD. In any case \*lowerBound is set to the calculated distance between mass centers on * return. Thus, if you want to calculate both distance between mass centers and EMD, \*lowerBound * should be set to 0. * a flow from `$$i$$` -th point of signature1 to `$$j$$` -th point of signature2 . */ + (float)EMD:(Mat*)signature1 signature2:(Mat*)signature2 distType:(DistanceTypes)distType cost:(Mat*)cost NS_SWIFT_NAME(wrapperEMD(signature1:signature2:distType:cost:)); /** * Computes the "minimal work" distance between two weighted point configurations. * * The function computes the earth mover distance and/or a lower boundary of the distance between the * two weighted point configurations. One of the applications described in CITE: RubnerSept98, * CITE: Rubner2000 is multi-dimensional histogram comparison for image retrieval. EMD is a transportation * problem that is solved using some modification of a simplex algorithm, thus the complexity is * exponential in the worst case, though, on average it is much faster. In the case of a real metric * the lower boundary can be calculated even faster (using linear-time algorithm) and it can be used * to determine roughly whether the two signatures are far enough so that they cannot relate to the * same object. * * @param signature1 First signature, a `$$\texttt{size1}\times \texttt{dims}+1$$` floating-point matrix. * Each row stores the point weight followed by the point coordinates. The matrix is allowed to have * a single column (weights only) if the user-defined cost matrix is used. The weights must be * non-negative and have at least one non-zero value. * @param signature2 Second signature of the same format as signature1 , though the number of rows * may be different. The total weights may be different. In this case an extra "dummy" point is added * to either signature1 or signature2. The weights must be non-negative and have at least one non-zero * value. * @param distType Used metric. See #DistanceTypes. * is used, lower boundary lowerBound cannot be calculated because it needs a metric function. * signatures that is a distance between mass centers. The lower boundary may not be calculated if * the user-defined cost matrix is used, the total weights of point configurations are not equal, or * if the signatures consist of weights only (the signature matrices have a single column). You * *must** initialize \*lowerBound . If the calculated distance between mass centers is greater or * equal to \*lowerBound (it means that the signatures are far enough), the function does not * calculate EMD. In any case \*lowerBound is set to the calculated distance between mass centers on * return. Thus, if you want to calculate both distance between mass centers and EMD, \*lowerBound * should be set to 0. * a flow from `$$i$$` -th point of signature1 to `$$j$$` -th point of signature2 . */ + (float)EMD:(Mat*)signature1 signature2:(Mat*)signature2 distType:(DistanceTypes)distType NS_SWIFT_NAME(wrapperEMD(signature1:signature2:distType:)); // // void cv::watershed(Mat image, Mat& markers) // /** * Performs a marker-based image segmentation using the watershed algorithm. * * The function implements one of the variants of watershed, non-parametric marker-based segmentation * algorithm, described in CITE: Meyer92 . * * Before passing the image to the function, you have to roughly outline the desired regions in the * image markers with positive (\>0) indices. So, every region is represented as one or more connected * components with the pixel values 1, 2, 3, and so on. Such markers can be retrieved from a binary * mask using #findContours and #drawContours (see the watershed.cpp demo). The markers are "seeds" of * the future image regions. All the other pixels in markers , whose relation to the outlined regions * is not known and should be defined by the algorithm, should be set to 0's. In the function output, * each pixel in markers is set to a value of the "seed" components or to -1 at boundaries between the * regions. * * NOTE: Any two neighbor connected components are not necessarily separated by a watershed boundary * (-1's pixels); for example, they can touch each other in the initial marker image passed to the * function. * * @param image Input 8-bit 3-channel image. * @param markers Input/output 32-bit single-channel image (map) of markers. It should have the same * size as image . * * @see `+findContours:contours:hierarchy:mode:method:offset:` */ + (void)watershed:(Mat*)image markers:(Mat*)markers NS_SWIFT_NAME(watershed(image:markers:)); // // void cv::pyrMeanShiftFiltering(Mat src, Mat& dst, double sp, double sr, int maxLevel = 1, TermCriteria termcrit = TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5,1)) // /** * Performs initial step of meanshift segmentation of an image. * * The function implements the filtering stage of meanshift segmentation, that is, the output of the * function is the filtered "posterized" image with color gradients and fine-grain texture flattened. * At every pixel (X,Y) of the input image (or down-sized input image, see below) the function executes * meanshift iterations, that is, the pixel (X,Y) neighborhood in the joint space-color hyperspace is * considered: * * `$$(x,y): X- \texttt{sp} \le x \le X+ \texttt{sp} , Y- \texttt{sp} \le y \le Y+ \texttt{sp} , ||(R,G,B)-(r,g,b)|| \le \texttt{sr}$$` * * where (R,G,B) and (r,g,b) are the vectors of color components at (X,Y) and (x,y), respectively * (though, the algorithm does not depend on the color space used, so any 3-component color space can * be used instead). Over the neighborhood the average spatial value (X',Y') and average color vector * (R',G',B') are found and they act as the neighborhood center on the next iteration: * * `$$(X,Y)~(X',Y'), (R,G,B)~(R',G',B').$$` * * After the iterations over, the color components of the initial pixel (that is, the pixel from where * the iterations started) are set to the final value (average color at the last iteration): * * `$$I(X,Y) <- (R*,G*,B*)$$` * * When maxLevel \> 0, the gaussian pyramid of maxLevel+1 levels is built, and the above procedure is * run on the smallest layer first. After that, the results are propagated to the larger layer and the * iterations are run again only on those pixels where the layer colors differ by more than sr from the * lower-resolution layer of the pyramid. That makes boundaries of color regions sharper. Note that the * results will be actually different from the ones obtained by running the meanshift procedure on the * whole original image (i.e. when maxLevel==0). * * @param src The source 8-bit, 3-channel image. * @param dst The destination image of the same format and the same size as the source. * @param sp The spatial window radius. * @param sr The color window radius. * @param maxLevel Maximum level of the pyramid for the segmentation. * @param termcrit Termination criteria: when to stop meanshift iterations. */ + (void)pyrMeanShiftFiltering:(Mat*)src dst:(Mat*)dst sp:(double)sp sr:(double)sr maxLevel:(int)maxLevel termcrit:(TermCriteria*)termcrit NS_SWIFT_NAME(pyrMeanShiftFiltering(src:dst:sp:sr:maxLevel:termcrit:)); /** * Performs initial step of meanshift segmentation of an image. * * The function implements the filtering stage of meanshift segmentation, that is, the output of the * function is the filtered "posterized" image with color gradients and fine-grain texture flattened. * At every pixel (X,Y) of the input image (or down-sized input image, see below) the function executes * meanshift iterations, that is, the pixel (X,Y) neighborhood in the joint space-color hyperspace is * considered: * * `$$(x,y): X- \texttt{sp} \le x \le X+ \texttt{sp} , Y- \texttt{sp} \le y \le Y+ \texttt{sp} , ||(R,G,B)-(r,g,b)|| \le \texttt{sr}$$` * * where (R,G,B) and (r,g,b) are the vectors of color components at (X,Y) and (x,y), respectively * (though, the algorithm does not depend on the color space used, so any 3-component color space can * be used instead). Over the neighborhood the average spatial value (X',Y') and average color vector * (R',G',B') are found and they act as the neighborhood center on the next iteration: * * `$$(X,Y)~(X',Y'), (R,G,B)~(R',G',B').$$` * * After the iterations over, the color components of the initial pixel (that is, the pixel from where * the iterations started) are set to the final value (average color at the last iteration): * * `$$I(X,Y) <- (R*,G*,B*)$$` * * When maxLevel \> 0, the gaussian pyramid of maxLevel+1 levels is built, and the above procedure is * run on the smallest layer first. After that, the results are propagated to the larger layer and the * iterations are run again only on those pixels where the layer colors differ by more than sr from the * lower-resolution layer of the pyramid. That makes boundaries of color regions sharper. Note that the * results will be actually different from the ones obtained by running the meanshift procedure on the * whole original image (i.e. when maxLevel==0). * * @param src The source 8-bit, 3-channel image. * @param dst The destination image of the same format and the same size as the source. * @param sp The spatial window radius. * @param sr The color window radius. * @param maxLevel Maximum level of the pyramid for the segmentation. */ + (void)pyrMeanShiftFiltering:(Mat*)src dst:(Mat*)dst sp:(double)sp sr:(double)sr maxLevel:(int)maxLevel NS_SWIFT_NAME(pyrMeanShiftFiltering(src:dst:sp:sr:maxLevel:)); /** * Performs initial step of meanshift segmentation of an image. * * The function implements the filtering stage of meanshift segmentation, that is, the output of the * function is the filtered "posterized" image with color gradients and fine-grain texture flattened. * At every pixel (X,Y) of the input image (or down-sized input image, see below) the function executes * meanshift iterations, that is, the pixel (X,Y) neighborhood in the joint space-color hyperspace is * considered: * * `$$(x,y): X- \texttt{sp} \le x \le X+ \texttt{sp} , Y- \texttt{sp} \le y \le Y+ \texttt{sp} , ||(R,G,B)-(r,g,b)|| \le \texttt{sr}$$` * * where (R,G,B) and (r,g,b) are the vectors of color components at (X,Y) and (x,y), respectively * (though, the algorithm does not depend on the color space used, so any 3-component color space can * be used instead). Over the neighborhood the average spatial value (X',Y') and average color vector * (R',G',B') are found and they act as the neighborhood center on the next iteration: * * `$$(X,Y)~(X',Y'), (R,G,B)~(R',G',B').$$` * * After the iterations over, the color components of the initial pixel (that is, the pixel from where * the iterations started) are set to the final value (average color at the last iteration): * * `$$I(X,Y) <- (R*,G*,B*)$$` * * When maxLevel \> 0, the gaussian pyramid of maxLevel+1 levels is built, and the above procedure is * run on the smallest layer first. After that, the results are propagated to the larger layer and the * iterations are run again only on those pixels where the layer colors differ by more than sr from the * lower-resolution layer of the pyramid. That makes boundaries of color regions sharper. Note that the * results will be actually different from the ones obtained by running the meanshift procedure on the * whole original image (i.e. when maxLevel==0). * * @param src The source 8-bit, 3-channel image. * @param dst The destination image of the same format and the same size as the source. * @param sp The spatial window radius. * @param sr The color window radius. */ + (void)pyrMeanShiftFiltering:(Mat*)src dst:(Mat*)dst sp:(double)sp sr:(double)sr NS_SWIFT_NAME(pyrMeanShiftFiltering(src:dst:sp:sr:)); // // void cv::grabCut(Mat img, Mat& mask, Rect rect, Mat& bgdModel, Mat& fgdModel, int iterCount, int mode = GC_EVAL) // /** * Runs the GrabCut algorithm. * * The function implements the [GrabCut image segmentation algorithm](http://en.wikipedia.org/wiki/GrabCut). * * @param img Input 8-bit 3-channel image. * @param mask Input/output 8-bit single-channel mask. The mask is initialized by the function when * mode is set to #GC_INIT_WITH_RECT. Its elements may have one of the #GrabCutClasses. * @param rect ROI containing a segmented object. The pixels outside of the ROI are marked as * "obvious background". The parameter is only used when mode==#GC_INIT_WITH_RECT . * @param bgdModel Temporary array for the background model. Do not modify it while you are * processing the same image. * @param fgdModel Temporary arrays for the foreground model. Do not modify it while you are * processing the same image. * @param iterCount Number of iterations the algorithm should make before returning the result. Note * that the result can be refined with further calls with mode==#GC_INIT_WITH_MASK or * mode==GC_EVAL . * @param mode Operation mode that could be one of the #GrabCutModes */ + (void)grabCut:(Mat*)img mask:(Mat*)mask rect:(Rect2i*)rect bgdModel:(Mat*)bgdModel fgdModel:(Mat*)fgdModel iterCount:(int)iterCount mode:(int)mode NS_SWIFT_NAME(grabCut(img:mask:rect:bgdModel:fgdModel:iterCount:mode:)); /** * Runs the GrabCut algorithm. * * The function implements the [GrabCut image segmentation algorithm](http://en.wikipedia.org/wiki/GrabCut). * * @param img Input 8-bit 3-channel image. * @param mask Input/output 8-bit single-channel mask. The mask is initialized by the function when * mode is set to #GC_INIT_WITH_RECT. Its elements may have one of the #GrabCutClasses. * @param rect ROI containing a segmented object. The pixels outside of the ROI are marked as * "obvious background". The parameter is only used when mode==#GC_INIT_WITH_RECT . * @param bgdModel Temporary array for the background model. Do not modify it while you are * processing the same image. * @param fgdModel Temporary arrays for the foreground model. Do not modify it while you are * processing the same image. * @param iterCount Number of iterations the algorithm should make before returning the result. Note * that the result can be refined with further calls with mode==#GC_INIT_WITH_MASK or * mode==GC_EVAL . */ + (void)grabCut:(Mat*)img mask:(Mat*)mask rect:(Rect2i*)rect bgdModel:(Mat*)bgdModel fgdModel:(Mat*)fgdModel iterCount:(int)iterCount NS_SWIFT_NAME(grabCut(img:mask:rect:bgdModel:fgdModel:iterCount:)); // // void cv::distanceTransform(Mat src, Mat& dst, Mat& labels, DistanceTypes distanceType, DistanceTransformMasks maskSize, DistanceTransformLabelTypes labelType = DIST_LABEL_CCOMP) // /** * Calculates the distance to the closest zero pixel for each pixel of the source image. * * The function cv::distanceTransform calculates the approximate or precise distance from every binary * image pixel to the nearest zero pixel. For zero image pixels, the distance will obviously be zero. * * When maskSize == #DIST_MASK_PRECISE and distanceType == #DIST_L2 , the function runs the * algorithm described in CITE: Felzenszwalb04 . This algorithm is parallelized with the TBB library. * * In other cases, the algorithm CITE: Borgefors86 is used. This means that for a pixel the function * finds the shortest path to the nearest zero pixel consisting of basic shifts: horizontal, vertical, * diagonal, or knight's move (the latest is available for a `$$5\times 5$$` mask). The overall * distance is calculated as a sum of these basic distances. Since the distance function should be * symmetric, all of the horizontal and vertical shifts must have the same cost (denoted as a ), all * the diagonal shifts must have the same cost (denoted as `b`), and all knight's moves must have the * same cost (denoted as `c`). For the #DIST_C and #DIST_L1 types, the distance is calculated * precisely, whereas for #DIST_L2 (Euclidean distance) the distance can be calculated only with a * relative error (a `$$5\times 5$$` mask gives more accurate results). For `a`,`b`, and `c`, OpenCV * uses the values suggested in the original paper: * - DIST_L1: `a = 1, b = 2` * - DIST_L2: * - `3 x 3`: `a=0.955, b=1.3693` * - `5 x 5`: `a=1, b=1.4, c=2.1969` * - DIST_C: `a = 1, b = 1` * * Typically, for a fast, coarse distance estimation #DIST_L2, a `$$3\times 3$$` mask is used. For a * more accurate distance estimation #DIST_L2, a `$$5\times 5$$` mask or the precise algorithm is used. * Note that both the precise and the approximate algorithms are linear on the number of pixels. * * This variant of the function does not only compute the minimum distance for each pixel `$$(x, y)$$` * but also identifies the nearest connected component consisting of zero pixels * (labelType==#DIST_LABEL_CCOMP) or the nearest zero pixel (labelType==#DIST_LABEL_PIXEL). Index of the * component/pixel is stored in `labels(x, y)`. When labelType==#DIST_LABEL_CCOMP, the function * automatically finds connected components of zero pixels in the input image and marks them with * distinct labels. When labelType==#DIST_LABEL_PIXEL, the function scans through the input image and * marks all the zero pixels with distinct labels. * * In this mode, the complexity is still linear. That is, the function provides a very fast way to * compute the Voronoi diagram for a binary image. Currently, the second variant can use only the * approximate distance transform algorithm, i.e. maskSize=#DIST_MASK_PRECISE is not supported * yet. * * @param src 8-bit, single-channel (binary) source image. * @param dst Output image with calculated distances. It is a 8-bit or 32-bit floating-point, * single-channel image of the same size as src. * @param labels Output 2D array of labels (the discrete Voronoi diagram). It has the type * CV_32SC1 and the same size as src. * @param distanceType Type of distance, see #DistanceTypes * @param maskSize Size of the distance transform mask, see #DistanceTransformMasks. * #DIST_MASK_PRECISE is not supported by this variant. In case of the #DIST_L1 or #DIST_C distance type, * the parameter is forced to 3 because a `$$3\times 3$$` mask gives the same result as `$$5\times * 5$$` or any larger aperture. * @param labelType Type of the label array to build, see #DistanceTransformLabelTypes. */ + (void)distanceTransformWithLabels:(Mat*)src dst:(Mat*)dst labels:(Mat*)labels distanceType:(DistanceTypes)distanceType maskSize:(DistanceTransformMasks)maskSize labelType:(DistanceTransformLabelTypes)labelType NS_SWIFT_NAME(distanceTransform(src:dst:labels:distanceType:maskSize:labelType:)); /** * Calculates the distance to the closest zero pixel for each pixel of the source image. * * The function cv::distanceTransform calculates the approximate or precise distance from every binary * image pixel to the nearest zero pixel. For zero image pixels, the distance will obviously be zero. * * When maskSize == #DIST_MASK_PRECISE and distanceType == #DIST_L2 , the function runs the * algorithm described in CITE: Felzenszwalb04 . This algorithm is parallelized with the TBB library. * * In other cases, the algorithm CITE: Borgefors86 is used. This means that for a pixel the function * finds the shortest path to the nearest zero pixel consisting of basic shifts: horizontal, vertical, * diagonal, or knight's move (the latest is available for a `$$5\times 5$$` mask). The overall * distance is calculated as a sum of these basic distances. Since the distance function should be * symmetric, all of the horizontal and vertical shifts must have the same cost (denoted as a ), all * the diagonal shifts must have the same cost (denoted as `b`), and all knight's moves must have the * same cost (denoted as `c`). For the #DIST_C and #DIST_L1 types, the distance is calculated * precisely, whereas for #DIST_L2 (Euclidean distance) the distance can be calculated only with a * relative error (a `$$5\times 5$$` mask gives more accurate results). For `a`,`b`, and `c`, OpenCV * uses the values suggested in the original paper: * - DIST_L1: `a = 1, b = 2` * - DIST_L2: * - `3 x 3`: `a=0.955, b=1.3693` * - `5 x 5`: `a=1, b=1.4, c=2.1969` * - DIST_C: `a = 1, b = 1` * * Typically, for a fast, coarse distance estimation #DIST_L2, a `$$3\times 3$$` mask is used. For a * more accurate distance estimation #DIST_L2, a `$$5\times 5$$` mask or the precise algorithm is used. * Note that both the precise and the approximate algorithms are linear on the number of pixels. * * This variant of the function does not only compute the minimum distance for each pixel `$$(x, y)$$` * but also identifies the nearest connected component consisting of zero pixels * (labelType==#DIST_LABEL_CCOMP) or the nearest zero pixel (labelType==#DIST_LABEL_PIXEL). Index of the * component/pixel is stored in `labels(x, y)`. When labelType==#DIST_LABEL_CCOMP, the function * automatically finds connected components of zero pixels in the input image and marks them with * distinct labels. When labelType==#DIST_LABEL_PIXEL, the function scans through the input image and * marks all the zero pixels with distinct labels. * * In this mode, the complexity is still linear. That is, the function provides a very fast way to * compute the Voronoi diagram for a binary image. Currently, the second variant can use only the * approximate distance transform algorithm, i.e. maskSize=#DIST_MASK_PRECISE is not supported * yet. * * @param src 8-bit, single-channel (binary) source image. * @param dst Output image with calculated distances. It is a 8-bit or 32-bit floating-point, * single-channel image of the same size as src. * @param labels Output 2D array of labels (the discrete Voronoi diagram). It has the type * CV_32SC1 and the same size as src. * @param distanceType Type of distance, see #DistanceTypes * @param maskSize Size of the distance transform mask, see #DistanceTransformMasks. * #DIST_MASK_PRECISE is not supported by this variant. In case of the #DIST_L1 or #DIST_C distance type, * the parameter is forced to 3 because a `$$3\times 3$$` mask gives the same result as `$$5\times * 5$$` or any larger aperture. */ + (void)distanceTransformWithLabels:(Mat*)src dst:(Mat*)dst labels:(Mat*)labels distanceType:(DistanceTypes)distanceType maskSize:(DistanceTransformMasks)maskSize NS_SWIFT_NAME(distanceTransform(src:dst:labels:distanceType:maskSize:)); // // void cv::distanceTransform(Mat src, Mat& dst, DistanceTypes distanceType, DistanceTransformMasks maskSize, int dstType = CV_32F) // /** * * @param src 8-bit, single-channel (binary) source image. * @param dst Output image with calculated distances. It is a 8-bit or 32-bit floating-point, * single-channel image of the same size as src . * @param distanceType Type of distance, see #DistanceTypes * @param maskSize Size of the distance transform mask, see #DistanceTransformMasks. In case of the * #DIST_L1 or #DIST_C distance type, the parameter is forced to 3 because a `$$3\times 3$$` mask gives * the same result as `$$5\times 5$$` or any larger aperture. * @param dstType Type of output image. It can be CV_8U or CV_32F. Type CV_8U can be used only for * the first variant of the function and distanceType == #DIST_L1. */ + (void)distanceTransform:(Mat*)src dst:(Mat*)dst distanceType:(DistanceTypes)distanceType maskSize:(DistanceTransformMasks)maskSize dstType:(int)dstType NS_SWIFT_NAME(distanceTransform(src:dst:distanceType:maskSize:dstType:)); /** * * @param src 8-bit, single-channel (binary) source image. * @param dst Output image with calculated distances. It is a 8-bit or 32-bit floating-point, * single-channel image of the same size as src . * @param distanceType Type of distance, see #DistanceTypes * @param maskSize Size of the distance transform mask, see #DistanceTransformMasks. In case of the * #DIST_L1 or #DIST_C distance type, the parameter is forced to 3 because a `$$3\times 3$$` mask gives * the same result as `$$5\times 5$$` or any larger aperture. * the first variant of the function and distanceType == #DIST_L1. */ + (void)distanceTransform:(Mat*)src dst:(Mat*)dst distanceType:(DistanceTypes)distanceType maskSize:(DistanceTransformMasks)maskSize NS_SWIFT_NAME(distanceTransform(src:dst:distanceType:maskSize:)); // // int cv::floodFill(Mat& image, Mat& mask, Point seedPoint, Scalar newVal, Rect* rect = 0, Scalar loDiff = Scalar(), Scalar upDiff = Scalar(), int flags = 4) // /** * Fills a connected component with the given color. * * The function cv::floodFill fills a connected component starting from the seed point with the specified * color. The connectivity is determined by the color/brightness closeness of the neighbor pixels. The * pixel at `$$(x,y)$$` is considered to belong to the repainted domain if: * * - in case of a grayscale image and floating range * `$$\texttt{src} (x',y')- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} (x',y')+ \texttt{upDiff}$$` * * * - in case of a grayscale image and fixed range * `$$\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)+ \texttt{upDiff}$$` * * * - in case of a color image and floating range * `$$\texttt{src} (x',y')_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} (x',y')_r+ \texttt{upDiff} _r,$$` * `$$\texttt{src} (x',y')_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} (x',y')_g+ \texttt{upDiff} _g$$` * and * `$$\texttt{src} (x',y')_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} (x',y')_b+ \texttt{upDiff} _b$$` * * * - in case of a color image and fixed range * `$$\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r+ \texttt{upDiff} _r,$$` * `$$\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g+ \texttt{upDiff} _g$$` * and * `$$\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b$$` * * * where `$$src(x',y')$$` is the value of one of pixel neighbors that is already known to belong to the * component. That is, to be added to the connected component, a color/brightness of the pixel should * be close enough to: * - Color/brightness of one of its neighbors that already belong to the connected component in case * of a floating range. * - Color/brightness of the seed point in case of a fixed range. * * Use these functions to either mark a connected component with the specified color in-place, or build * a mask and then extract the contour, or copy the region to another image, and so on. * * @param image Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the * function unless the #FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See * the details below. * @param mask Operation mask that should be a single-channel 8-bit image, 2 pixels wider and 2 pixels * taller than image. If an empty Mat is passed it will be created automatically. Since this is both an * input and output parameter, you must take responsibility of initializing it. * Flood-filling cannot go across non-zero pixels in the input mask. For example, * an edge detector output can be used as a mask to stop filling at edges. On output, pixels in the * mask corresponding to filled pixels in the image are set to 1 or to the specified value in flags * as described below. Additionally, the function fills the border of the mask with ones to simplify * internal processing. It is therefore possible to use the same mask in multiple calls to the function * to make sure the filled areas do not overlap. * @param seedPoint Starting point. * @param newVal New value of the repainted domain pixels. * @param loDiff Maximal lower brightness/color difference between the currently observed pixel and * one of its neighbors belonging to the component, or a seed pixel being added to the component. * @param upDiff Maximal upper brightness/color difference between the currently observed pixel and * one of its neighbors belonging to the component, or a seed pixel being added to the component. * @param rect Optional output parameter set by the function to the minimum bounding rectangle of the * repainted domain. * @param flags Operation flags. The first 8 bits contain a connectivity value. The default value of * 4 means that only the four nearest neighbor pixels (those that share an edge) are considered. A * connectivity value of 8 means that the eight nearest neighbor pixels (those that share a corner) * will be considered. The next 8 bits (8-16) contain a value between 1 and 255 with which to fill * the mask (the default value is 1). For example, 4 | ( 255 \<\< 8 ) will consider 4 nearest * neighbours and fill the mask with a value of 255. The following additional options occupy higher * bits and therefore may be further combined with the connectivity and mask fill values using * bit-wise or (|), see #FloodFillFlags. * * NOTE: Since the mask is larger than the filled image, a pixel `$$(x, y)$$` in image corresponds to the * pixel `$$(x+1, y+1)$$` in the mask . * * @see `+findContours:contours:hierarchy:mode:method:offset:` */ + (int)floodFill:(Mat*)image mask:(Mat*)mask seedPoint:(Point2i*)seedPoint newVal:(Scalar*)newVal rect:(Rect2i*)rect loDiff:(Scalar*)loDiff upDiff:(Scalar*)upDiff flags:(int)flags NS_SWIFT_NAME(floodFill(image:mask:seedPoint:newVal:rect:loDiff:upDiff:flags:)); /** * Fills a connected component with the given color. * * The function cv::floodFill fills a connected component starting from the seed point with the specified * color. The connectivity is determined by the color/brightness closeness of the neighbor pixels. The * pixel at `$$(x,y)$$` is considered to belong to the repainted domain if: * * - in case of a grayscale image and floating range * `$$\texttt{src} (x',y')- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} (x',y')+ \texttt{upDiff}$$` * * * - in case of a grayscale image and fixed range * `$$\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)+ \texttt{upDiff}$$` * * * - in case of a color image and floating range * `$$\texttt{src} (x',y')_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} (x',y')_r+ \texttt{upDiff} _r,$$` * `$$\texttt{src} (x',y')_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} (x',y')_g+ \texttt{upDiff} _g$$` * and * `$$\texttt{src} (x',y')_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} (x',y')_b+ \texttt{upDiff} _b$$` * * * - in case of a color image and fixed range * `$$\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r+ \texttt{upDiff} _r,$$` * `$$\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g+ \texttt{upDiff} _g$$` * and * `$$\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b$$` * * * where `$$src(x',y')$$` is the value of one of pixel neighbors that is already known to belong to the * component. That is, to be added to the connected component, a color/brightness of the pixel should * be close enough to: * - Color/brightness of one of its neighbors that already belong to the connected component in case * of a floating range. * - Color/brightness of the seed point in case of a fixed range. * * Use these functions to either mark a connected component with the specified color in-place, or build * a mask and then extract the contour, or copy the region to another image, and so on. * * @param image Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the * function unless the #FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See * the details below. * @param mask Operation mask that should be a single-channel 8-bit image, 2 pixels wider and 2 pixels * taller than image. If an empty Mat is passed it will be created automatically. Since this is both an * input and output parameter, you must take responsibility of initializing it. * Flood-filling cannot go across non-zero pixels in the input mask. For example, * an edge detector output can be used as a mask to stop filling at edges. On output, pixels in the * mask corresponding to filled pixels in the image are set to 1 or to the specified value in flags * as described below. Additionally, the function fills the border of the mask with ones to simplify * internal processing. It is therefore possible to use the same mask in multiple calls to the function * to make sure the filled areas do not overlap. * @param seedPoint Starting point. * @param newVal New value of the repainted domain pixels. * @param loDiff Maximal lower brightness/color difference between the currently observed pixel and * one of its neighbors belonging to the component, or a seed pixel being added to the component. * @param upDiff Maximal upper brightness/color difference between the currently observed pixel and * one of its neighbors belonging to the component, or a seed pixel being added to the component. * @param rect Optional output parameter set by the function to the minimum bounding rectangle of the * repainted domain. * 4 means that only the four nearest neighbor pixels (those that share an edge) are considered. A * connectivity value of 8 means that the eight nearest neighbor pixels (those that share a corner) * will be considered. The next 8 bits (8-16) contain a value between 1 and 255 with which to fill * the mask (the default value is 1). For example, 4 | ( 255 \<\< 8 ) will consider 4 nearest * neighbours and fill the mask with a value of 255. The following additional options occupy higher * bits and therefore may be further combined with the connectivity and mask fill values using * bit-wise or (|), see #FloodFillFlags. * * NOTE: Since the mask is larger than the filled image, a pixel `$$(x, y)$$` in image corresponds to the * pixel `$$(x+1, y+1)$$` in the mask . * * @see `+findContours:contours:hierarchy:mode:method:offset:` */ + (int)floodFill:(Mat*)image mask:(Mat*)mask seedPoint:(Point2i*)seedPoint newVal:(Scalar*)newVal rect:(Rect2i*)rect loDiff:(Scalar*)loDiff upDiff:(Scalar*)upDiff NS_SWIFT_NAME(floodFill(image:mask:seedPoint:newVal:rect:loDiff:upDiff:)); /** * Fills a connected component with the given color. * * The function cv::floodFill fills a connected component starting from the seed point with the specified * color. The connectivity is determined by the color/brightness closeness of the neighbor pixels. The * pixel at `$$(x,y)$$` is considered to belong to the repainted domain if: * * - in case of a grayscale image and floating range * `$$\texttt{src} (x',y')- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} (x',y')+ \texttt{upDiff}$$` * * * - in case of a grayscale image and fixed range * `$$\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)+ \texttt{upDiff}$$` * * * - in case of a color image and floating range * `$$\texttt{src} (x',y')_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} (x',y')_r+ \texttt{upDiff} _r,$$` * `$$\texttt{src} (x',y')_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} (x',y')_g+ \texttt{upDiff} _g$$` * and * `$$\texttt{src} (x',y')_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} (x',y')_b+ \texttt{upDiff} _b$$` * * * - in case of a color image and fixed range * `$$\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r+ \texttt{upDiff} _r,$$` * `$$\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g+ \texttt{upDiff} _g$$` * and * `$$\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b$$` * * * where `$$src(x',y')$$` is the value of one of pixel neighbors that is already known to belong to the * component. That is, to be added to the connected component, a color/brightness of the pixel should * be close enough to: * - Color/brightness of one of its neighbors that already belong to the connected component in case * of a floating range. * - Color/brightness of the seed point in case of a fixed range. * * Use these functions to either mark a connected component with the specified color in-place, or build * a mask and then extract the contour, or copy the region to another image, and so on. * * @param image Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the * function unless the #FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See * the details below. * @param mask Operation mask that should be a single-channel 8-bit image, 2 pixels wider and 2 pixels * taller than image. If an empty Mat is passed it will be created automatically. Since this is both an * input and output parameter, you must take responsibility of initializing it. * Flood-filling cannot go across non-zero pixels in the input mask. For example, * an edge detector output can be used as a mask to stop filling at edges. On output, pixels in the * mask corresponding to filled pixels in the image are set to 1 or to the specified value in flags * as described below. Additionally, the function fills the border of the mask with ones to simplify * internal processing. It is therefore possible to use the same mask in multiple calls to the function * to make sure the filled areas do not overlap. * @param seedPoint Starting point. * @param newVal New value of the repainted domain pixels. * @param loDiff Maximal lower brightness/color difference between the currently observed pixel and * one of its neighbors belonging to the component, or a seed pixel being added to the component. * one of its neighbors belonging to the component, or a seed pixel being added to the component. * @param rect Optional output parameter set by the function to the minimum bounding rectangle of the * repainted domain. * 4 means that only the four nearest neighbor pixels (those that share an edge) are considered. A * connectivity value of 8 means that the eight nearest neighbor pixels (those that share a corner) * will be considered. The next 8 bits (8-16) contain a value between 1 and 255 with which to fill * the mask (the default value is 1). For example, 4 | ( 255 \<\< 8 ) will consider 4 nearest * neighbours and fill the mask with a value of 255. The following additional options occupy higher * bits and therefore may be further combined with the connectivity and mask fill values using * bit-wise or (|), see #FloodFillFlags. * * NOTE: Since the mask is larger than the filled image, a pixel `$$(x, y)$$` in image corresponds to the * pixel `$$(x+1, y+1)$$` in the mask . * * @see `+findContours:contours:hierarchy:mode:method:offset:` */ + (int)floodFill:(Mat*)image mask:(Mat*)mask seedPoint:(Point2i*)seedPoint newVal:(Scalar*)newVal rect:(Rect2i*)rect loDiff:(Scalar*)loDiff NS_SWIFT_NAME(floodFill(image:mask:seedPoint:newVal:rect:loDiff:)); /** * Fills a connected component with the given color. * * The function cv::floodFill fills a connected component starting from the seed point with the specified * color. The connectivity is determined by the color/brightness closeness of the neighbor pixels. The * pixel at `$$(x,y)$$` is considered to belong to the repainted domain if: * * - in case of a grayscale image and floating range * `$$\texttt{src} (x',y')- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} (x',y')+ \texttt{upDiff}$$` * * * - in case of a grayscale image and fixed range * `$$\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)+ \texttt{upDiff}$$` * * * - in case of a color image and floating range * `$$\texttt{src} (x',y')_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} (x',y')_r+ \texttt{upDiff} _r,$$` * `$$\texttt{src} (x',y')_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} (x',y')_g+ \texttt{upDiff} _g$$` * and * `$$\texttt{src} (x',y')_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} (x',y')_b+ \texttt{upDiff} _b$$` * * * - in case of a color image and fixed range * `$$\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r+ \texttt{upDiff} _r,$$` * `$$\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g+ \texttt{upDiff} _g$$` * and * `$$\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b$$` * * * where `$$src(x',y')$$` is the value of one of pixel neighbors that is already known to belong to the * component. That is, to be added to the connected component, a color/brightness of the pixel should * be close enough to: * - Color/brightness of one of its neighbors that already belong to the connected component in case * of a floating range. * - Color/brightness of the seed point in case of a fixed range. * * Use these functions to either mark a connected component with the specified color in-place, or build * a mask and then extract the contour, or copy the region to another image, and so on. * * @param image Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the * function unless the #FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See * the details below. * @param mask Operation mask that should be a single-channel 8-bit image, 2 pixels wider and 2 pixels * taller than image. If an empty Mat is passed it will be created automatically. Since this is both an * input and output parameter, you must take responsibility of initializing it. * Flood-filling cannot go across non-zero pixels in the input mask. For example, * an edge detector output can be used as a mask to stop filling at edges. On output, pixels in the * mask corresponding to filled pixels in the image are set to 1 or to the specified value in flags * as described below. Additionally, the function fills the border of the mask with ones to simplify * internal processing. It is therefore possible to use the same mask in multiple calls to the function * to make sure the filled areas do not overlap. * @param seedPoint Starting point. * @param newVal New value of the repainted domain pixels. * one of its neighbors belonging to the component, or a seed pixel being added to the component. * one of its neighbors belonging to the component, or a seed pixel being added to the component. * @param rect Optional output parameter set by the function to the minimum bounding rectangle of the * repainted domain. * 4 means that only the four nearest neighbor pixels (those that share an edge) are considered. A * connectivity value of 8 means that the eight nearest neighbor pixels (those that share a corner) * will be considered. The next 8 bits (8-16) contain a value between 1 and 255 with which to fill * the mask (the default value is 1). For example, 4 | ( 255 \<\< 8 ) will consider 4 nearest * neighbours and fill the mask with a value of 255. The following additional options occupy higher * bits and therefore may be further combined with the connectivity and mask fill values using * bit-wise or (|), see #FloodFillFlags. * * NOTE: Since the mask is larger than the filled image, a pixel `$$(x, y)$$` in image corresponds to the * pixel `$$(x+1, y+1)$$` in the mask . * * @see `+findContours:contours:hierarchy:mode:method:offset:` */ + (int)floodFill:(Mat*)image mask:(Mat*)mask seedPoint:(Point2i*)seedPoint newVal:(Scalar*)newVal rect:(Rect2i*)rect NS_SWIFT_NAME(floodFill(image:mask:seedPoint:newVal:rect:)); /** * Fills a connected component with the given color. * * The function cv::floodFill fills a connected component starting from the seed point with the specified * color. The connectivity is determined by the color/brightness closeness of the neighbor pixels. The * pixel at `$$(x,y)$$` is considered to belong to the repainted domain if: * * - in case of a grayscale image and floating range * `$$\texttt{src} (x',y')- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} (x',y')+ \texttt{upDiff}$$` * * * - in case of a grayscale image and fixed range * `$$\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)+ \texttt{upDiff}$$` * * * - in case of a color image and floating range * `$$\texttt{src} (x',y')_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} (x',y')_r+ \texttt{upDiff} _r,$$` * `$$\texttt{src} (x',y')_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} (x',y')_g+ \texttt{upDiff} _g$$` * and * `$$\texttt{src} (x',y')_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} (x',y')_b+ \texttt{upDiff} _b$$` * * * - in case of a color image and fixed range * `$$\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r+ \texttt{upDiff} _r,$$` * `$$\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g+ \texttt{upDiff} _g$$` * and * `$$\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b$$` * * * where `$$src(x',y')$$` is the value of one of pixel neighbors that is already known to belong to the * component. That is, to be added to the connected component, a color/brightness of the pixel should * be close enough to: * - Color/brightness of one of its neighbors that already belong to the connected component in case * of a floating range. * - Color/brightness of the seed point in case of a fixed range. * * Use these functions to either mark a connected component with the specified color in-place, or build * a mask and then extract the contour, or copy the region to another image, and so on. * * @param image Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the * function unless the #FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See * the details below. * @param mask Operation mask that should be a single-channel 8-bit image, 2 pixels wider and 2 pixels * taller than image. If an empty Mat is passed it will be created automatically. Since this is both an * input and output parameter, you must take responsibility of initializing it. * Flood-filling cannot go across non-zero pixels in the input mask. For example, * an edge detector output can be used as a mask to stop filling at edges. On output, pixels in the * mask corresponding to filled pixels in the image are set to 1 or to the specified value in flags * as described below. Additionally, the function fills the border of the mask with ones to simplify * internal processing. It is therefore possible to use the same mask in multiple calls to the function * to make sure the filled areas do not overlap. * @param seedPoint Starting point. * @param newVal New value of the repainted domain pixels. * one of its neighbors belonging to the component, or a seed pixel being added to the component. * one of its neighbors belonging to the component, or a seed pixel being added to the component. * repainted domain. * 4 means that only the four nearest neighbor pixels (those that share an edge) are considered. A * connectivity value of 8 means that the eight nearest neighbor pixels (those that share a corner) * will be considered. The next 8 bits (8-16) contain a value between 1 and 255 with which to fill * the mask (the default value is 1). For example, 4 | ( 255 \<\< 8 ) will consider 4 nearest * neighbours and fill the mask with a value of 255. The following additional options occupy higher * bits and therefore may be further combined with the connectivity and mask fill values using * bit-wise or (|), see #FloodFillFlags. * * NOTE: Since the mask is larger than the filled image, a pixel `$$(x, y)$$` in image corresponds to the * pixel `$$(x+1, y+1)$$` in the mask . * * @see `+findContours:contours:hierarchy:mode:method:offset:` */ + (int)floodFill:(Mat*)image mask:(Mat*)mask seedPoint:(Point2i*)seedPoint newVal:(Scalar*)newVal NS_SWIFT_NAME(floodFill(image:mask:seedPoint:newVal:)); // // void cv::blendLinear(Mat src1, Mat src2, Mat weights1, Mat weights2, Mat& dst) // /** * * * variant without `mask` parameter */ + (void)blendLinear:(Mat*)src1 src2:(Mat*)src2 weights1:(Mat*)weights1 weights2:(Mat*)weights2 dst:(Mat*)dst NS_SWIFT_NAME(blendLinear(src1:src2:weights1:weights2:dst:)); // // void cv::cvtColor(Mat src, Mat& dst, ColorConversionCodes code, int dstCn = 0) // /** * Converts an image from one color space to another. * * The function converts an input image from one color space to another. In case of a transformation * to-from RGB color space, the order of the channels should be specified explicitly (RGB or BGR). Note * that the default color format in OpenCV is often referred to as RGB but it is actually BGR (the * bytes are reversed). So the first byte in a standard (24-bit) color image will be an 8-bit Blue * component, the second byte will be Green, and the third byte will be Red. The fourth, fifth, and * sixth bytes would then be the second pixel (Blue, then Green, then Red), and so on. * * The conventional ranges for R, G, and B channel values are: * - 0 to 255 for CV_8U images * - 0 to 65535 for CV_16U images * - 0 to 1 for CV_32F images * * In case of linear transformations, the range does not matter. But in case of a non-linear * transformation, an input RGB image should be normalized to the proper value range to get the correct * results, for example, for RGB `$$\rightarrow$$` L\*u\*v\* transformation. For example, if you have a * 32-bit floating-point image directly converted from an 8-bit image without any scaling, then it will * have the 0..255 value range instead of 0..1 assumed by the function. So, before calling #cvtColor , * you need first to scale the image down: * * img *= 1./255; * cvtColor(img, img, COLOR_BGR2Luv); * * If you use #cvtColor with 8-bit images, the conversion will have some information lost. For many * applications, this will not be noticeable but it is recommended to use 32-bit images in applications * that need the full range of colors or that convert an image before an operation and then convert * back. * * If conversion adds the alpha channel, its value will set to the maximum of corresponding channel * range: 255 for CV_8U, 65535 for CV_16U, 1 for CV_32F. * * @param src input image: 8-bit unsigned, 16-bit unsigned ( CV_16UC... ), or single-precision * floating-point. * @param dst output image of the same size and depth as src. * @param code color space conversion code (see #ColorConversionCodes). * @param dstCn number of channels in the destination image; if the parameter is 0, the number of the * channels is derived automatically from src and code. * * @see `REF: imgproc_color_conversions` */ + (void)cvtColor:(Mat*)src dst:(Mat*)dst code:(ColorConversionCodes)code dstCn:(int)dstCn NS_SWIFT_NAME(cvtColor(src:dst:code:dstCn:)); /** * Converts an image from one color space to another. * * The function converts an input image from one color space to another. In case of a transformation * to-from RGB color space, the order of the channels should be specified explicitly (RGB or BGR). Note * that the default color format in OpenCV is often referred to as RGB but it is actually BGR (the * bytes are reversed). So the first byte in a standard (24-bit) color image will be an 8-bit Blue * component, the second byte will be Green, and the third byte will be Red. The fourth, fifth, and * sixth bytes would then be the second pixel (Blue, then Green, then Red), and so on. * * The conventional ranges for R, G, and B channel values are: * - 0 to 255 for CV_8U images * - 0 to 65535 for CV_16U images * - 0 to 1 for CV_32F images * * In case of linear transformations, the range does not matter. But in case of a non-linear * transformation, an input RGB image should be normalized to the proper value range to get the correct * results, for example, for RGB `$$\rightarrow$$` L\*u\*v\* transformation. For example, if you have a * 32-bit floating-point image directly converted from an 8-bit image without any scaling, then it will * have the 0..255 value range instead of 0..1 assumed by the function. So, before calling #cvtColor , * you need first to scale the image down: * * img *= 1./255; * cvtColor(img, img, COLOR_BGR2Luv); * * If you use #cvtColor with 8-bit images, the conversion will have some information lost. For many * applications, this will not be noticeable but it is recommended to use 32-bit images in applications * that need the full range of colors or that convert an image before an operation and then convert * back. * * If conversion adds the alpha channel, its value will set to the maximum of corresponding channel * range: 255 for CV_8U, 65535 for CV_16U, 1 for CV_32F. * * @param src input image: 8-bit unsigned, 16-bit unsigned ( CV_16UC... ), or single-precision * floating-point. * @param dst output image of the same size and depth as src. * @param code color space conversion code (see #ColorConversionCodes). * channels is derived automatically from src and code. * * @see `REF: imgproc_color_conversions` */ + (void)cvtColor:(Mat*)src dst:(Mat*)dst code:(ColorConversionCodes)code NS_SWIFT_NAME(cvtColor(src:dst:code:)); // // void cv::cvtColorTwoPlane(Mat src1, Mat src2, Mat& dst, int code) // /** * Converts an image from one color space to another where the source image is * stored in two planes. * * This function only supports YUV420 to RGB conversion as of now. * * - #COLOR_YUV2BGR_NV12 * - #COLOR_YUV2RGB_NV12 * - #COLOR_YUV2BGRA_NV12 * - #COLOR_YUV2RGBA_NV12 * - #COLOR_YUV2BGR_NV21 * - #COLOR_YUV2RGB_NV21 * - #COLOR_YUV2BGRA_NV21 * - #COLOR_YUV2RGBA_NV21 */ + (void)cvtColorTwoPlane:(Mat*)src1 src2:(Mat*)src2 dst:(Mat*)dst code:(int)code NS_SWIFT_NAME(cvtColorTwoPlane(src1:src2:dst:code:)); // // void cv::demosaicing(Mat src, Mat& dst, int code, int dstCn = 0) // /** * main function for all demosaicing processes * * @param src input image: 8-bit unsigned or 16-bit unsigned. * @param dst output image of the same size and depth as src. * @param code Color space conversion code (see the description below). * @param dstCn number of channels in the destination image; if the parameter is 0, the number of the * channels is derived automatically from src and code. * * The function can do the following transformations: * * - Demosaicing using bilinear interpolation * * #COLOR_BayerBG2BGR , #COLOR_BayerGB2BGR , #COLOR_BayerRG2BGR , #COLOR_BayerGR2BGR * * #COLOR_BayerBG2GRAY , #COLOR_BayerGB2GRAY , #COLOR_BayerRG2GRAY , #COLOR_BayerGR2GRAY * * - Demosaicing using Variable Number of Gradients. * * #COLOR_BayerBG2BGR_VNG , #COLOR_BayerGB2BGR_VNG , #COLOR_BayerRG2BGR_VNG , #COLOR_BayerGR2BGR_VNG * * - Edge-Aware Demosaicing. * * #COLOR_BayerBG2BGR_EA , #COLOR_BayerGB2BGR_EA , #COLOR_BayerRG2BGR_EA , #COLOR_BayerGR2BGR_EA * * - Demosaicing with alpha channel * * #COLOR_BayerBG2BGRA , #COLOR_BayerGB2BGRA , #COLOR_BayerRG2BGRA , #COLOR_BayerGR2BGRA * * @see `+cvtColor:dst:code:dstCn:` */ + (void)demosaicing:(Mat*)src dst:(Mat*)dst code:(int)code dstCn:(int)dstCn NS_SWIFT_NAME(demosaicing(src:dst:code:dstCn:)); /** * main function for all demosaicing processes * * @param src input image: 8-bit unsigned or 16-bit unsigned. * @param dst output image of the same size and depth as src. * @param code Color space conversion code (see the description below). * channels is derived automatically from src and code. * * The function can do the following transformations: * * - Demosaicing using bilinear interpolation * * #COLOR_BayerBG2BGR , #COLOR_BayerGB2BGR , #COLOR_BayerRG2BGR , #COLOR_BayerGR2BGR * * #COLOR_BayerBG2GRAY , #COLOR_BayerGB2GRAY , #COLOR_BayerRG2GRAY , #COLOR_BayerGR2GRAY * * - Demosaicing using Variable Number of Gradients. * * #COLOR_BayerBG2BGR_VNG , #COLOR_BayerGB2BGR_VNG , #COLOR_BayerRG2BGR_VNG , #COLOR_BayerGR2BGR_VNG * * - Edge-Aware Demosaicing. * * #COLOR_BayerBG2BGR_EA , #COLOR_BayerGB2BGR_EA , #COLOR_BayerRG2BGR_EA , #COLOR_BayerGR2BGR_EA * * - Demosaicing with alpha channel * * #COLOR_BayerBG2BGRA , #COLOR_BayerGB2BGRA , #COLOR_BayerRG2BGRA , #COLOR_BayerGR2BGRA * * @see `+cvtColor:dst:code:dstCn:` */ + (void)demosaicing:(Mat*)src dst:(Mat*)dst code:(int)code NS_SWIFT_NAME(demosaicing(src:dst:code:)); // // Moments cv::moments(Mat array, bool binaryImage = false) // /** * Calculates all of the moments up to the third order of a polygon or rasterized shape. * * The function computes moments, up to the 3rd order, of a vector shape or a rasterized shape. The * results are returned in the structure cv::Moments. * * @param array Raster image (single-channel, 8-bit or floating-point 2D array) or an array ( * `$$1 \times N$$` or `$$N \times 1$$` ) of 2D points (Point or Point2f ). * @param binaryImage If it is true, all non-zero image pixels are treated as 1's. The parameter is * used for images only. * @return moments. * * NOTE: Only applicable to contour moments calculations from Python bindings: Note that the numpy * type for the input array should be either np.int32 or np.float32. * * @see `+contourArea:oriented:`, `+arcLength:closed:` */ + (Moments*)moments:(Mat*)array binaryImage:(BOOL)binaryImage NS_SWIFT_NAME(moments(array:binaryImage:)); /** * Calculates all of the moments up to the third order of a polygon or rasterized shape. * * The function computes moments, up to the 3rd order, of a vector shape or a rasterized shape. The * results are returned in the structure cv::Moments. * * @param array Raster image (single-channel, 8-bit or floating-point 2D array) or an array ( * `$$1 \times N$$` or `$$N \times 1$$` ) of 2D points (Point or Point2f ). * used for images only. * @return moments. * * NOTE: Only applicable to contour moments calculations from Python bindings: Note that the numpy * type for the input array should be either np.int32 or np.float32. * * @see `+contourArea:oriented:`, `+arcLength:closed:` */ + (Moments*)moments:(Mat*)array NS_SWIFT_NAME(moments(array:)); // // void cv::HuMoments(Moments m, Mat& hu) // + (void)HuMoments:(Moments*)m hu:(Mat*)hu NS_SWIFT_NAME(HuMoments(m:hu:)); // // void cv::matchTemplate(Mat image, Mat templ, Mat& result, TemplateMatchModes method, Mat mask = Mat()) // /** * Compares a template against overlapped image regions. * * The function slides through image , compares the overlapped patches of size `$$w \times h$$` against * templ using the specified method and stores the comparison results in result . #TemplateMatchModes * describes the formulae for the available comparison methods ( `$$I$$` denotes image, `$$T$$` * template, `$$R$$` result, `$$M$$` the optional mask ). The summation is done over template and/or * the image patch: `$$x' = 0...w-1, y' = 0...h-1$$` * * After the function finishes the comparison, the best matches can be found as global minimums (when * #TM_SQDIFF was used) or maximums (when #TM_CCORR or #TM_CCOEFF was used) using the * #minMaxLoc function. In case of a color image, template summation in the numerator and each sum in * the denominator is done over all of the channels and separate mean values are used for each channel. * That is, the function can take a color template and a color image. The result will still be a * single-channel image, which is easier to analyze. * * @param image Image where the search is running. It must be 8-bit or 32-bit floating-point. * @param templ Searched template. It must be not greater than the source image and have the same * data type. * @param result Map of comparison results. It must be single-channel 32-bit floating-point. If image * is `$$W \times H$$` and templ is `$$w \times h$$` , then result is `$$(W-w+1) \times (H-h+1)$$` . * @param method Parameter specifying the comparison method, see #TemplateMatchModes * @param mask Optional mask. It must have the same size as templ. It must either have the same number * of channels as template or only one channel, which is then used for all template and * image channels. If the data type is #CV_8U, the mask is interpreted as a binary mask, * meaning only elements where mask is nonzero are used and are kept unchanged independent * of the actual mask value (weight equals 1). For data tpye #CV_32F, the mask values are * used as weights. The exact formulas are documented in #TemplateMatchModes. */ + (void)matchTemplate:(Mat*)image templ:(Mat*)templ result:(Mat*)result method:(TemplateMatchModes)method mask:(Mat*)mask NS_SWIFT_NAME(matchTemplate(image:templ:result:method:mask:)); /** * Compares a template against overlapped image regions. * * The function slides through image , compares the overlapped patches of size `$$w \times h$$` against * templ using the specified method and stores the comparison results in result . #TemplateMatchModes * describes the formulae for the available comparison methods ( `$$I$$` denotes image, `$$T$$` * template, `$$R$$` result, `$$M$$` the optional mask ). The summation is done over template and/or * the image patch: `$$x' = 0...w-1, y' = 0...h-1$$` * * After the function finishes the comparison, the best matches can be found as global minimums (when * #TM_SQDIFF was used) or maximums (when #TM_CCORR or #TM_CCOEFF was used) using the * #minMaxLoc function. In case of a color image, template summation in the numerator and each sum in * the denominator is done over all of the channels and separate mean values are used for each channel. * That is, the function can take a color template and a color image. The result will still be a * single-channel image, which is easier to analyze. * * @param image Image where the search is running. It must be 8-bit or 32-bit floating-point. * @param templ Searched template. It must be not greater than the source image and have the same * data type. * @param result Map of comparison results. It must be single-channel 32-bit floating-point. If image * is `$$W \times H$$` and templ is `$$w \times h$$` , then result is `$$(W-w+1) \times (H-h+1)$$` . * @param method Parameter specifying the comparison method, see #TemplateMatchModes * of channels as template or only one channel, which is then used for all template and * image channels. If the data type is #CV_8U, the mask is interpreted as a binary mask, * meaning only elements where mask is nonzero are used and are kept unchanged independent * of the actual mask value (weight equals 1). For data tpye #CV_32F, the mask values are * used as weights. The exact formulas are documented in #TemplateMatchModes. */ + (void)matchTemplate:(Mat*)image templ:(Mat*)templ result:(Mat*)result method:(TemplateMatchModes)method NS_SWIFT_NAME(matchTemplate(image:templ:result:method:)); // // int cv::connectedComponents(Mat image, Mat& labels, int connectivity, int ltype, int ccltype) // /** * computes the connected components labeled image of boolean image * * image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0 * represents the background label. ltype specifies the output label image type, an important * consideration based on the total number of labels or alternatively the total number of pixels in * the source image. ccltype specifies the connected components labeling algorithm to use, currently * Bolelli (Spaghetti) CITE: Bolelli2019, Grana (BBDT) CITE: Grana2010 and Wu's (SAUF) CITE: Wu2009 algorithms * are supported, see the #ConnectedComponentsAlgorithmsTypes for details. Note that SAUF algorithm forces * a row major ordering of labels while Spaghetti and BBDT do not. * This function uses parallel version of the algorithms if at least one allowed * parallel framework is enabled and if the rows of the image are at least twice the number returned by #getNumberOfCPUs. * * @param image the 8-bit single-channel image to be labeled * @param labels destination labeled image * @param connectivity 8 or 4 for 8-way or 4-way connectivity respectively * @param ltype output image label type. Currently CV_32S and CV_16U are supported. * @param ccltype connected components algorithm type (see the #ConnectedComponentsAlgorithmsTypes). */ + (int)connectedComponentsWithAlgorithm:(Mat*)image labels:(Mat*)labels connectivity:(int)connectivity ltype:(int)ltype ccltype:(int)ccltype NS_SWIFT_NAME(connectedComponents(image:labels:connectivity:ltype:ccltype:)); // // int cv::connectedComponents(Mat image, Mat& labels, int connectivity = 8, int ltype = CV_32S) // /** * * * @param image the 8-bit single-channel image to be labeled * @param labels destination labeled image * @param connectivity 8 or 4 for 8-way or 4-way connectivity respectively * @param ltype output image label type. Currently CV_32S and CV_16U are supported. */ + (int)connectedComponents:(Mat*)image labels:(Mat*)labels connectivity:(int)connectivity ltype:(int)ltype NS_SWIFT_NAME(connectedComponents(image:labels:connectivity:ltype:)); /** * * * @param image the 8-bit single-channel image to be labeled * @param labels destination labeled image * @param connectivity 8 or 4 for 8-way or 4-way connectivity respectively */ + (int)connectedComponents:(Mat*)image labels:(Mat*)labels connectivity:(int)connectivity NS_SWIFT_NAME(connectedComponents(image:labels:connectivity:)); /** * * * @param image the 8-bit single-channel image to be labeled * @param labels destination labeled image */ + (int)connectedComponents:(Mat*)image labels:(Mat*)labels NS_SWIFT_NAME(connectedComponents(image:labels:)); // // int cv::connectedComponentsWithStats(Mat image, Mat& labels, Mat& stats, Mat& centroids, int connectivity, int ltype, ConnectedComponentsAlgorithmsTypes ccltype) // /** * computes the connected components labeled image of boolean image and also produces a statistics output for each label * * image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0 * represents the background label. ltype specifies the output label image type, an important * consideration based on the total number of labels or alternatively the total number of pixels in * the source image. ccltype specifies the connected components labeling algorithm to use, currently * Bolelli (Spaghetti) CITE: Bolelli2019, Grana (BBDT) CITE: Grana2010 and Wu's (SAUF) CITE: Wu2009 algorithms * are supported, see the #ConnectedComponentsAlgorithmsTypes for details. Note that SAUF algorithm forces * a row major ordering of labels while Spaghetti and BBDT do not. * This function uses parallel version of the algorithms (statistics included) if at least one allowed * parallel framework is enabled and if the rows of the image are at least twice the number returned by #getNumberOfCPUs. * * @param image the 8-bit single-channel image to be labeled * @param labels destination labeled image * @param stats statistics output for each label, including the background label. * Statistics are accessed via stats(label, COLUMN) where COLUMN is one of * #ConnectedComponentsTypes, selecting the statistic. The data type is CV_32S. * @param centroids centroid output for each label, including the background label. Centroids are * accessed via centroids(label, 0) for x and centroids(label, 1) for y. The data type CV_64F. * @param connectivity 8 or 4 for 8-way or 4-way connectivity respectively * @param ltype output image label type. Currently CV_32S and CV_16U are supported. * @param ccltype connected components algorithm type (see #ConnectedComponentsAlgorithmsTypes). */ + (int)connectedComponentsWithStatsWithAlgorithm:(Mat*)image labels:(Mat*)labels stats:(Mat*)stats centroids:(Mat*)centroids connectivity:(int)connectivity ltype:(int)ltype ccltype:(ConnectedComponentsAlgorithmsTypes)ccltype NS_SWIFT_NAME(connectedComponentsWithStats(image:labels:stats:centroids:connectivity:ltype:ccltype:)); // // int cv::connectedComponentsWithStats(Mat image, Mat& labels, Mat& stats, Mat& centroids, int connectivity = 8, int ltype = CV_32S) // /** * * @param image the 8-bit single-channel image to be labeled * @param labels destination labeled image * @param stats statistics output for each label, including the background label. * Statistics are accessed via stats(label, COLUMN) where COLUMN is one of * #ConnectedComponentsTypes, selecting the statistic. The data type is CV_32S. * @param centroids centroid output for each label, including the background label. Centroids are * accessed via centroids(label, 0) for x and centroids(label, 1) for y. The data type CV_64F. * @param connectivity 8 or 4 for 8-way or 4-way connectivity respectively * @param ltype output image label type. Currently CV_32S and CV_16U are supported. */ + (int)connectedComponentsWithStats:(Mat*)image labels:(Mat*)labels stats:(Mat*)stats centroids:(Mat*)centroids connectivity:(int)connectivity ltype:(int)ltype NS_SWIFT_NAME(connectedComponentsWithStats(image:labels:stats:centroids:connectivity:ltype:)); /** * * @param image the 8-bit single-channel image to be labeled * @param labels destination labeled image * @param stats statistics output for each label, including the background label. * Statistics are accessed via stats(label, COLUMN) where COLUMN is one of * #ConnectedComponentsTypes, selecting the statistic. The data type is CV_32S. * @param centroids centroid output for each label, including the background label. Centroids are * accessed via centroids(label, 0) for x and centroids(label, 1) for y. The data type CV_64F. * @param connectivity 8 or 4 for 8-way or 4-way connectivity respectively */ + (int)connectedComponentsWithStats:(Mat*)image labels:(Mat*)labels stats:(Mat*)stats centroids:(Mat*)centroids connectivity:(int)connectivity NS_SWIFT_NAME(connectedComponentsWithStats(image:labels:stats:centroids:connectivity:)); /** * * @param image the 8-bit single-channel image to be labeled * @param labels destination labeled image * @param stats statistics output for each label, including the background label. * Statistics are accessed via stats(label, COLUMN) where COLUMN is one of * #ConnectedComponentsTypes, selecting the statistic. The data type is CV_32S. * @param centroids centroid output for each label, including the background label. Centroids are * accessed via centroids(label, 0) for x and centroids(label, 1) for y. The data type CV_64F. */ + (int)connectedComponentsWithStats:(Mat*)image labels:(Mat*)labels stats:(Mat*)stats centroids:(Mat*)centroids NS_SWIFT_NAME(connectedComponentsWithStats(image:labels:stats:centroids:)); // // void cv::findContours(Mat image, vector_vector_Point& contours, Mat& hierarchy, RetrievalModes mode, ContourApproximationModes method, Point offset = Point()) // /** * Finds contours in a binary image. * * The function retrieves contours from the binary image using the algorithm CITE: Suzuki85 . The contours * are a useful tool for shape analysis and object detection and recognition. See squares.cpp in the * OpenCV sample directory. * NOTE: Since opencv 3.2 source image is not modified by this function. * * @param image Source, an 8-bit single-channel image. Non-zero pixels are treated as 1's. Zero * pixels remain 0's, so the image is treated as binary . You can use #compare, #inRange, #threshold , * #adaptiveThreshold, #Canny, and others to create a binary image out of a grayscale or color one. * If mode equals to #RETR_CCOMP or #RETR_FLOODFILL, the input can also be a 32-bit integer image of labels (CV_32SC1). * @param contours Detected contours. Each contour is stored as a vector of points (e.g. * std::vector >). * @param hierarchy Optional output vector (e.g. std::vector), containing information about the image topology. It has * as many elements as the number of contours. For each i-th contour contours[i], the elements * hierarchy[i][0] , hierarchy[i][1] , hierarchy[i][2] , and hierarchy[i][3] are set to 0-based indices * in contours of the next and previous contours at the same hierarchical level, the first child * contour and the parent contour, respectively. If for the contour i there are no next, previous, * parent, or nested contours, the corresponding elements of hierarchy[i] will be negative. * NOTE: In Python, hierarchy is nested inside a top level array. Use hierarchy[0][i] to access hierarchical elements of i-th contour. * @param mode Contour retrieval mode, see #RetrievalModes * @param method Contour approximation method, see #ContourApproximationModes * @param offset Optional offset by which every contour point is shifted. This is useful if the * contours are extracted from the image ROI and then they should be analyzed in the whole image * context. */ + (void)findContours:(Mat*)image contours:(NSMutableArray*>*)contours hierarchy:(Mat*)hierarchy mode:(RetrievalModes)mode method:(ContourApproximationModes)method offset:(Point2i*)offset NS_SWIFT_NAME(findContours(image:contours:hierarchy:mode:method:offset:)); /** * Finds contours in a binary image. * * The function retrieves contours from the binary image using the algorithm CITE: Suzuki85 . The contours * are a useful tool for shape analysis and object detection and recognition. See squares.cpp in the * OpenCV sample directory. * NOTE: Since opencv 3.2 source image is not modified by this function. * * @param image Source, an 8-bit single-channel image. Non-zero pixels are treated as 1's. Zero * pixels remain 0's, so the image is treated as binary . You can use #compare, #inRange, #threshold , * #adaptiveThreshold, #Canny, and others to create a binary image out of a grayscale or color one. * If mode equals to #RETR_CCOMP or #RETR_FLOODFILL, the input can also be a 32-bit integer image of labels (CV_32SC1). * @param contours Detected contours. Each contour is stored as a vector of points (e.g. * std::vector >). * @param hierarchy Optional output vector (e.g. std::vector), containing information about the image topology. It has * as many elements as the number of contours. For each i-th contour contours[i], the elements * hierarchy[i][0] , hierarchy[i][1] , hierarchy[i][2] , and hierarchy[i][3] are set to 0-based indices * in contours of the next and previous contours at the same hierarchical level, the first child * contour and the parent contour, respectively. If for the contour i there are no next, previous, * parent, or nested contours, the corresponding elements of hierarchy[i] will be negative. * NOTE: In Python, hierarchy is nested inside a top level array. Use hierarchy[0][i] to access hierarchical elements of i-th contour. * @param mode Contour retrieval mode, see #RetrievalModes * @param method Contour approximation method, see #ContourApproximationModes * contours are extracted from the image ROI and then they should be analyzed in the whole image * context. */ + (void)findContours:(Mat*)image contours:(NSMutableArray*>*)contours hierarchy:(Mat*)hierarchy mode:(RetrievalModes)mode method:(ContourApproximationModes)method NS_SWIFT_NAME(findContours(image:contours:hierarchy:mode:method:)); // // void cv::approxPolyDP(vector_Point2f curve, vector_Point2f& approxCurve, double epsilon, bool closed) // /** * Approximates a polygonal curve(s) with the specified precision. * * The function cv::approxPolyDP approximates a curve or a polygon with another curve/polygon with less * vertices so that the distance between them is less or equal to the specified precision. It uses the * Douglas-Peucker algorithm * * @param curve Input vector of a 2D point stored in std::vector or Mat * @param approxCurve Result of the approximation. The type should match the type of the input curve. * @param epsilon Parameter specifying the approximation accuracy. This is the maximum distance * between the original curve and its approximation. * @param closed If true, the approximated curve is closed (its first and last vertices are * connected). Otherwise, it is not closed. */ + (void)approxPolyDP:(NSArray*)curve approxCurve:(NSMutableArray*)approxCurve epsilon:(double)epsilon closed:(BOOL)closed NS_SWIFT_NAME(approxPolyDP(curve:approxCurve:epsilon:closed:)); // // double cv::arcLength(vector_Point2f curve, bool closed) // /** * Calculates a contour perimeter or a curve length. * * The function computes a curve length or a closed contour perimeter. * * @param curve Input vector of 2D points, stored in std::vector or Mat. * @param closed Flag indicating whether the curve is closed or not. */ + (double)arcLength:(NSArray*)curve closed:(BOOL)closed NS_SWIFT_NAME(arcLength(curve:closed:)); // // Rect cv::boundingRect(Mat array) // /** * Calculates the up-right bounding rectangle of a point set or non-zero pixels of gray-scale image. * * The function calculates and returns the minimal up-right bounding rectangle for the specified point set or * non-zero pixels of gray-scale image. * * @param array Input gray-scale image or 2D point set, stored in std::vector or Mat. */ + (Rect2i*)boundingRect:(Mat*)array NS_SWIFT_NAME(boundingRect(array:)); // // double cv::contourArea(Mat contour, bool oriented = false) // /** * Calculates a contour area. * * The function computes a contour area. Similarly to moments , the area is computed using the Green * formula. Thus, the returned area and the number of non-zero pixels, if you draw the contour using * #drawContours or #fillPoly , can be different. Also, the function will most certainly give a wrong * results for contours with self-intersections. * * Example: * * vector contour; * contour.push_back(Point2f(0, 0)); * contour.push_back(Point2f(10, 0)); * contour.push_back(Point2f(10, 10)); * contour.push_back(Point2f(5, 4)); * * double area0 = contourArea(contour); * vector approx; * approxPolyDP(contour, approx, 5, true); * double area1 = contourArea(approx); * * cout << "area0 =" << area0 << endl << * "area1 =" << area1 << endl << * "approx poly vertices" << approx.size() << endl; * * @param contour Input vector of 2D points (contour vertices), stored in std::vector or Mat. * @param oriented Oriented area flag. If it is true, the function returns a signed area value, * depending on the contour orientation (clockwise or counter-clockwise). Using this feature you can * determine orientation of a contour by taking the sign of an area. By default, the parameter is * false, which means that the absolute value is returned. */ + (double)contourArea:(Mat*)contour oriented:(BOOL)oriented NS_SWIFT_NAME(contourArea(contour:oriented:)); /** * Calculates a contour area. * * The function computes a contour area. Similarly to moments , the area is computed using the Green * formula. Thus, the returned area and the number of non-zero pixels, if you draw the contour using * #drawContours or #fillPoly , can be different. Also, the function will most certainly give a wrong * results for contours with self-intersections. * * Example: * * vector contour; * contour.push_back(Point2f(0, 0)); * contour.push_back(Point2f(10, 0)); * contour.push_back(Point2f(10, 10)); * contour.push_back(Point2f(5, 4)); * * double area0 = contourArea(contour); * vector approx; * approxPolyDP(contour, approx, 5, true); * double area1 = contourArea(approx); * * cout << "area0 =" << area0 << endl << * "area1 =" << area1 << endl << * "approx poly vertices" << approx.size() << endl; * * @param contour Input vector of 2D points (contour vertices), stored in std::vector or Mat. * depending on the contour orientation (clockwise or counter-clockwise). Using this feature you can * determine orientation of a contour by taking the sign of an area. By default, the parameter is * false, which means that the absolute value is returned. */ + (double)contourArea:(Mat*)contour NS_SWIFT_NAME(contourArea(contour:)); // // RotatedRect cv::minAreaRect(vector_Point2f points) // /** * Finds a rotated rectangle of the minimum area enclosing the input 2D point set. * * The function calculates and returns the minimum-area bounding rectangle (possibly rotated) for a * specified point set. Developer should keep in mind that the returned RotatedRect can contain negative * indices when data is close to the containing Mat element boundary. * * @param points Input vector of 2D points, stored in std::vector\<\> or Mat */ + (RotatedRect*)minAreaRect:(NSArray*)points NS_SWIFT_NAME(minAreaRect(points:)); // // void cv::boxPoints(RotatedRect box, Mat& points) // /** * Finds the four vertices of a rotated rect. Useful to draw the rotated rectangle. * * The function finds the four vertices of a rotated rectangle. This function is useful to draw the * rectangle. In C++, instead of using this function, you can directly use RotatedRect::points method. Please * visit the REF: tutorial_bounding_rotated_ellipses "tutorial on Creating Bounding rotated boxes and ellipses for contours" for more information. * * @param box The input rotated rectangle. It may be the output of * @param points The output array of four vertices of rectangles. */ + (void)boxPoints:(RotatedRect*)box points:(Mat*)points NS_SWIFT_NAME(boxPoints(box:points:)); // // void cv::minEnclosingCircle(vector_Point2f points, Point2f& center, float& radius) // /** * Finds a circle of the minimum area enclosing a 2D point set. * * The function finds the minimal enclosing circle of a 2D point set using an iterative algorithm. * * @param points Input vector of 2D points, stored in std::vector\<\> or Mat * @param center Output center of the circle. * @param radius Output radius of the circle. */ + (void)minEnclosingCircle:(NSArray*)points center:(Point2f*)center radius:(float*)radius NS_SWIFT_NAME(minEnclosingCircle(points:center:radius:)); // // double cv::minEnclosingTriangle(Mat points, Mat& triangle) // /** * Finds a triangle of minimum area enclosing a 2D point set and returns its area. * * The function finds a triangle of minimum area enclosing the given set of 2D points and returns its * area. The output for a given 2D point set is shown in the image below. 2D points are depicted in * red* and the enclosing triangle in *yellow*. * * ![Sample output of the minimum enclosing triangle function](pics/minenclosingtriangle.png) * * The implementation of the algorithm is based on O'Rourke's CITE: ORourke86 and Klee and Laskowski's * CITE: KleeLaskowski85 papers. O'Rourke provides a `$$\theta(n)$$` algorithm for finding the minimal * enclosing triangle of a 2D convex polygon with n vertices. Since the #minEnclosingTriangle function * takes a 2D point set as input an additional preprocessing step of computing the convex hull of the * 2D point set is required. The complexity of the #convexHull function is `$$O(n log(n))$$` which is higher * than `$$\theta(n)$$`. Thus the overall complexity of the function is `$$O(n log(n))$$`. * * @param points Input vector of 2D points with depth CV_32S or CV_32F, stored in std::vector\<\> or Mat * @param triangle Output vector of three 2D points defining the vertices of the triangle. The depth * of the OutputArray must be CV_32F. */ + (double)minEnclosingTriangle:(Mat*)points triangle:(Mat*)triangle NS_SWIFT_NAME(minEnclosingTriangle(points:triangle:)); // // double cv::matchShapes(Mat contour1, Mat contour2, ShapeMatchModes method, double parameter) // /** * Compares two shapes. * * The function compares two shapes. All three implemented methods use the Hu invariants (see #HuMoments) * * @param contour1 First contour or grayscale image. * @param contour2 Second contour or grayscale image. * @param method Comparison method, see #ShapeMatchModes * @param parameter Method-specific parameter (not supported now). */ + (double)matchShapes:(Mat*)contour1 contour2:(Mat*)contour2 method:(ShapeMatchModes)method parameter:(double)parameter NS_SWIFT_NAME(matchShapes(contour1:contour2:method:parameter:)); // // void cv::convexHull(vector_Point points, vector_int& hull, bool clockwise = false, _hidden_ returnPoints = true) // /** * Finds the convex hull of a point set. * * The function cv::convexHull finds the convex hull of a 2D point set using the Sklansky's algorithm CITE: Sklansky82 * that has *O(N logN)* complexity in the current implementation. * * @param points Input 2D point set, stored in std::vector or Mat. * @param hull Output convex hull. It is either an integer vector of indices or vector of points. In * the first case, the hull elements are 0-based indices of the convex hull points in the original * array (since the set of convex hull points is a subset of the original point set). In the second * case, hull elements are the convex hull points themselves. * @param clockwise Orientation flag. If it is true, the output convex hull is oriented clockwise. * Otherwise, it is oriented counter-clockwise. The assumed coordinate system has its X axis pointing * to the right, and its Y axis pointing upwards. * @param returnPoints Operation flag. In case of a matrix, when the flag is true, the function * returns convex hull points. Otherwise, it returns indices of the convex hull points. When the * output array is std::vector, the flag is ignored, and the output depends on the type of the * vector: std::vector\ implies returnPoints=false, std::vector\ implies * returnPoints=true. * * NOTE: `points` and `hull` should be different arrays, inplace processing isn't supported. * * Check REF: tutorial_hull "the corresponding tutorial" for more details. * * useful links: * * https://www.learnopencv.com/convex-hull-using-opencv-in-python-and-c/ */ + (void)convexHull:(NSArray*)points hull:(IntVector*)hull clockwise:(BOOL)clockwise NS_SWIFT_NAME(convexHull(points:hull:clockwise:)); /** * Finds the convex hull of a point set. * * The function cv::convexHull finds the convex hull of a 2D point set using the Sklansky's algorithm CITE: Sklansky82 * that has *O(N logN)* complexity in the current implementation. * * @param points Input 2D point set, stored in std::vector or Mat. * @param hull Output convex hull. It is either an integer vector of indices or vector of points. In * the first case, the hull elements are 0-based indices of the convex hull points in the original * array (since the set of convex hull points is a subset of the original point set). In the second * case, hull elements are the convex hull points themselves. * Otherwise, it is oriented counter-clockwise. The assumed coordinate system has its X axis pointing * to the right, and its Y axis pointing upwards. * returns convex hull points. Otherwise, it returns indices of the convex hull points. When the * output array is std::vector, the flag is ignored, and the output depends on the type of the * vector: std::vector\ implies returnPoints=false, std::vector\ implies * returnPoints=true. * * NOTE: `points` and `hull` should be different arrays, inplace processing isn't supported. * * Check REF: tutorial_hull "the corresponding tutorial" for more details. * * useful links: * * https://www.learnopencv.com/convex-hull-using-opencv-in-python-and-c/ */ + (void)convexHull:(NSArray*)points hull:(IntVector*)hull NS_SWIFT_NAME(convexHull(points:hull:)); // // void cv::convexityDefects(vector_Point contour, vector_int convexhull, vector_Vec4i& convexityDefects) // /** * Finds the convexity defects of a contour. * * The figure below displays convexity defects of a hand contour: * * ![image](pics/defects.png) * * @param contour Input contour. * @param convexhull Convex hull obtained using convexHull that should contain indices of the contour * points that make the hull. * @param convexityDefects The output vector of convexity defects. In C++ and the new Python/Java * interface each convexity defect is represented as 4-element integer vector (a.k.a. #Vec4i): * (start_index, end_index, farthest_pt_index, fixpt_depth), where indices are 0-based indices * in the original contour of the convexity defect beginning, end and the farthest point, and * fixpt_depth is fixed-point approximation (with 8 fractional bits) of the distance between the * farthest contour point and the hull. That is, to get the floating-point value of the depth will be * fixpt_depth/256.0. */ + (void)convexityDefects:(NSArray*)contour convexhull:(IntVector*)convexhull convexityDefects:(NSMutableArray*)convexityDefects NS_SWIFT_NAME(convexityDefects(contour:convexhull:convexityDefects:)); // // bool cv::isContourConvex(vector_Point contour) // /** * Tests a contour convexity. * * The function tests whether the input contour is convex or not. The contour must be simple, that is, * without self-intersections. Otherwise, the function output is undefined. * * @param contour Input vector of 2D points, stored in std::vector\<\> or Mat */ + (BOOL)isContourConvex:(NSArray*)contour NS_SWIFT_NAME(isContourConvex(contour:)); // // float cv::intersectConvexConvex(Mat p1, Mat p2, Mat& p12, bool handleNested = true) // /** * Finds intersection of two convex polygons * * @param p1 First polygon * @param p2 Second polygon * @param p12 Output polygon describing the intersecting area * @param handleNested When true, an intersection is found if one of the polygons is fully enclosed in the other. * When false, no intersection is found. If the polygons share a side or the vertex of one polygon lies on an edge * of the other, they are not considered nested and an intersection will be found regardless of the value of handleNested. * * @return Absolute value of area of intersecting polygon * * NOTE: intersectConvexConvex doesn't confirm that both polygons are convex and will return invalid results if they aren't. */ + (float)intersectConvexConvex:(Mat*)p1 p2:(Mat*)p2 p12:(Mat*)p12 handleNested:(BOOL)handleNested NS_SWIFT_NAME(intersectConvexConvex(p1:p2:p12:handleNested:)); /** * Finds intersection of two convex polygons * * @param p1 First polygon * @param p2 Second polygon * @param p12 Output polygon describing the intersecting area * When false, no intersection is found. If the polygons share a side or the vertex of one polygon lies on an edge * of the other, they are not considered nested and an intersection will be found regardless of the value of handleNested. * * @return Absolute value of area of intersecting polygon * * NOTE: intersectConvexConvex doesn't confirm that both polygons are convex and will return invalid results if they aren't. */ + (float)intersectConvexConvex:(Mat*)p1 p2:(Mat*)p2 p12:(Mat*)p12 NS_SWIFT_NAME(intersectConvexConvex(p1:p2:p12:)); // // RotatedRect cv::fitEllipse(vector_Point2f points) // /** * Fits an ellipse around a set of 2D points. * * The function calculates the ellipse that fits (in a least-squares sense) a set of 2D points best of * all. It returns the rotated rectangle in which the ellipse is inscribed. The first algorithm described by CITE: Fitzgibbon95 * is used. Developer should keep in mind that it is possible that the returned * ellipse/rotatedRect data contains negative indices, due to the data points being close to the * border of the containing Mat element. * * @param points Input 2D point set, stored in std::vector\<\> or Mat */ + (RotatedRect*)fitEllipse:(NSArray*)points NS_SWIFT_NAME(fitEllipse(points:)); // // RotatedRect cv::fitEllipseAMS(Mat points) // /** * Fits an ellipse around a set of 2D points. * * The function calculates the ellipse that fits a set of 2D points. * It returns the rotated rectangle in which the ellipse is inscribed. * The Approximate Mean Square (AMS) proposed by CITE: Taubin1991 is used. * * For an ellipse, this basis set is `$$ \chi= \left(x^2, x y, y^2, x, y, 1\right) $$`, * which is a set of six free coefficients `$$ A^T=\left\{A_{\text{xx}},A_{\text{xy}},A_{\text{yy}},A_x,A_y,A_0\right\} $$`. * However, to specify an ellipse, all that is needed is five numbers; the major and minor axes lengths `$$ (a,b) $$`, * the position `$$ (x_0,y_0) $$`, and the orientation `$$ \theta $$`. This is because the basis set includes lines, * quadratics, parabolic and hyperbolic functions as well as elliptical functions as possible fits. * If the fit is found to be a parabolic or hyperbolic function then the standard #fitEllipse method is used. * The AMS method restricts the fit to parabolic, hyperbolic and elliptical curves * by imposing the condition that `$$ A^T ( D_x^T D_x + D_y^T D_y) A = 1 $$` where * the matrices `$$ Dx $$` and `$$ Dy $$` are the partial derivatives of the design matrix `$$ D $$` with * respect to x and y. The matrices are formed row by row applying the following to * each of the points in the set: * `$$\begin{aligned} * D(i,:)&=\left\{x_i^2, x_i y_i, y_i^2, x_i, y_i, 1\right\} & * D_x(i,:)&=\left\{2 x_i,y_i,0,1,0,0\right\} & * D_y(i,:)&=\left\{0,x_i,2 y_i,0,1,0\right\} * \end{aligned}$$` * The AMS method minimizes the cost function * `$$\begin{aligned} * \epsilon ^2=\frac{ A^T D^T D A }{ A^T (D_x^T D_x + D_y^T D_y) A^T } * \end{aligned}$$` * * The minimum cost is found by solving the generalized eigenvalue problem. * * `$$\begin{aligned} * D^T D A = \lambda \left( D_x^T D_x + D_y^T D_y\right) A * \end{aligned}$$` * * @param points Input 2D point set, stored in std::vector\<\> or Mat */ + (RotatedRect*)fitEllipseAMS:(Mat*)points NS_SWIFT_NAME(fitEllipseAMS(points:)); // // RotatedRect cv::fitEllipseDirect(Mat points) // /** * Fits an ellipse around a set of 2D points. * * The function calculates the ellipse that fits a set of 2D points. * It returns the rotated rectangle in which the ellipse is inscribed. * The Direct least square (Direct) method by CITE: Fitzgibbon1999 is used. * * For an ellipse, this basis set is `$$ \chi= \left(x^2, x y, y^2, x, y, 1\right) $$`, * which is a set of six free coefficients `$$ A^T=\left\{A_{\text{xx}},A_{\text{xy}},A_{\text{yy}},A_x,A_y,A_0\right\} $$`. * However, to specify an ellipse, all that is needed is five numbers; the major and minor axes lengths `$$ (a,b) $$`, * the position `$$ (x_0,y_0) $$`, and the orientation `$$ \theta $$`. This is because the basis set includes lines, * quadratics, parabolic and hyperbolic functions as well as elliptical functions as possible fits. * The Direct method confines the fit to ellipses by ensuring that `$$ 4 A_{xx} A_{yy}- A_{xy}^2 > 0 $$`. * The condition imposed is that `$$ 4 A_{xx} A_{yy}- A_{xy}^2=1 $$` which satisfies the inequality * and as the coefficients can be arbitrarily scaled is not overly restrictive. * * `$$\begin{aligned} * \epsilon ^2= A^T D^T D A \quad \text{with} \quad A^T C A =1 \quad \text{and} \quad C=\left(\begin{matrix} * 0 & 0 & 2 & 0 & 0 & 0 \\ * 0 & -1 & 0 & 0 & 0 & 0 \\ * 2 & 0 & 0 & 0 & 0 & 0 \\ * 0 & 0 & 0 & 0 & 0 & 0 \\ * 0 & 0 & 0 & 0 & 0 & 0 \\ * 0 & 0 & 0 & 0 & 0 & 0 * \end{matrix} \right) * \end{aligned}$$` * * The minimum cost is found by solving the generalized eigenvalue problem. * * `$$\begin{aligned} * D^T D A = \lambda \left( C\right) A * \end{aligned}$$` * * The system produces only one positive eigenvalue `$$ \lambda$$` which is chosen as the solution * with its eigenvector `$$\mathbf{u}$$`. These are used to find the coefficients * * `$$\begin{aligned} * A = \sqrt{\frac{1}{\mathbf{u}^T C \mathbf{u}}} \mathbf{u} * \end{aligned}$$` * The scaling factor guarantees that `$$A^T C A =1$$`. * * @param points Input 2D point set, stored in std::vector\<\> or Mat */ + (RotatedRect*)fitEllipseDirect:(Mat*)points NS_SWIFT_NAME(fitEllipseDirect(points:)); // // void cv::fitLine(Mat points, Mat& line, DistanceTypes distType, double param, double reps, double aeps) // /** * Fits a line to a 2D or 3D point set. * * The function fitLine fits a line to a 2D or 3D point set by minimizing `$$\sum_i \rho(r_i)$$` where * `$$r_i$$` is a distance between the `$$i^{th}$$` point, the line and `$$\rho(r)$$` is a distance function, one * of the following: * - DIST_L2 * `$$\rho (r) = r^2/2 \quad \text{(the simplest and the fastest least-squares method)}$$` * - DIST_L1 * `$$\rho (r) = r$$` * - DIST_L12 * `$$\rho (r) = 2 \cdot ( \sqrt{1 + \frac{r^2}{2}} - 1)$$` * - DIST_FAIR * `$$\rho \left (r \right ) = C^2 \cdot \left ( \frac{r}{C} - \log{\left(1 + \frac{r}{C}\right)} \right ) \quad \text{where} \quad C=1.3998$$` * - DIST_WELSCH * `$$\rho \left (r \right ) = \frac{C^2}{2} \cdot \left ( 1 - \exp{\left(-\left(\frac{r}{C}\right)^2\right)} \right ) \quad \text{where} \quad C=2.9846$$` * - DIST_HUBER * `$$\newcommand{\fork}[4]{ \left\{ \begin{array}{l l} #1 & \text{#2}\\\\ #3 & \text{#4}\\\\ \end{array} \right.} \rho (r) = \fork{r^2/2}{if \(r < C\)}{C \cdot (r-C/2)}{otherwise} \quad \text{where} \quad C=1.345$$` * * The algorithm is based on the M-estimator ( ) technique * that iteratively fits the line using the weighted least-squares algorithm. After each iteration the * weights `$$w_i$$` are adjusted to be inversely proportional to `$$\rho(r_i)$$` . * * @param points Input vector of 2D or 3D points, stored in std::vector\<\> or Mat. * @param line Output line parameters. In case of 2D fitting, it should be a vector of 4 elements * (like Vec4f) - (vx, vy, x0, y0), where (vx, vy) is a normalized vector collinear to the line and * (x0, y0) is a point on the line. In case of 3D fitting, it should be a vector of 6 elements (like * Vec6f) - (vx, vy, vz, x0, y0, z0), where (vx, vy, vz) is a normalized vector collinear to the line * and (x0, y0, z0) is a point on the line. * @param distType Distance used by the M-estimator, see #DistanceTypes * @param param Numerical parameter ( C ) for some types of distances. If it is 0, an optimal value * is chosen. * @param reps Sufficient accuracy for the radius (distance between the coordinate origin and the line). * @param aeps Sufficient accuracy for the angle. 0.01 would be a good default value for reps and aeps. */ + (void)fitLine:(Mat*)points line:(Mat*)line distType:(DistanceTypes)distType param:(double)param reps:(double)reps aeps:(double)aeps NS_SWIFT_NAME(fitLine(points:line:distType:param:reps:aeps:)); // // double cv::pointPolygonTest(vector_Point2f contour, Point2f pt, bool measureDist) // /** * Performs a point-in-contour test. * * The function determines whether the point is inside a contour, outside, or lies on an edge (or * coincides with a vertex). It returns positive (inside), negative (outside), or zero (on an edge) * value, correspondingly. When measureDist=false , the return value is +1, -1, and 0, respectively. * Otherwise, the return value is a signed distance between the point and the nearest contour edge. * * See below a sample output of the function where each image pixel is tested against the contour: * * ![sample output](pics/pointpolygon.png) * * @param contour Input contour. * @param pt Point tested against the contour. * @param measureDist If true, the function estimates the signed distance from the point to the * nearest contour edge. Otherwise, the function only checks if the point is inside a contour or not. */ + (double)pointPolygonTest:(NSArray*)contour pt:(Point2f*)pt measureDist:(BOOL)measureDist NS_SWIFT_NAME(pointPolygonTest(contour:pt:measureDist:)); // // int cv::rotatedRectangleIntersection(RotatedRect rect1, RotatedRect rect2, Mat& intersectingRegion) // /** * Finds out if there is any intersection between two rotated rectangles. * * If there is then the vertices of the intersecting region are returned as well. * * Below are some examples of intersection configurations. The hatched pattern indicates the * intersecting region and the red vertices are returned by the function. * * ![intersection examples](pics/intersection.png) * * @param rect1 First rectangle * @param rect2 Second rectangle * @param intersectingRegion The output array of the vertices of the intersecting region. It returns * at most 8 vertices. Stored as std::vector\ or cv::Mat as Mx1 of type CV_32FC2. * @return One of #RectanglesIntersectTypes */ + (int)rotatedRectangleIntersection:(RotatedRect*)rect1 rect2:(RotatedRect*)rect2 intersectingRegion:(Mat*)intersectingRegion NS_SWIFT_NAME(rotatedRectangleIntersection(rect1:rect2:intersectingRegion:)); // // Ptr_GeneralizedHoughBallard cv::createGeneralizedHoughBallard() // /** * Creates a smart pointer to a cv::GeneralizedHoughBallard class and initializes it. */ + (GeneralizedHoughBallard*)createGeneralizedHoughBallard NS_SWIFT_NAME(createGeneralizedHoughBallard()); // // Ptr_GeneralizedHoughGuil cv::createGeneralizedHoughGuil() // /** * Creates a smart pointer to a cv::GeneralizedHoughGuil class and initializes it. */ + (GeneralizedHoughGuil*)createGeneralizedHoughGuil NS_SWIFT_NAME(createGeneralizedHoughGuil()); // // void cv::applyColorMap(Mat src, Mat& dst, ColormapTypes colormap) // /** * Applies a GNU Octave/MATLAB equivalent colormap on a given image. * * @param src The source image, grayscale or colored of type CV_8UC1 or CV_8UC3. * @param dst The result is the colormapped source image. Note: Mat::create is called on dst. * @param colormap The colormap to apply, see #ColormapTypes */ + (void)applyColorMap:(Mat*)src dst:(Mat*)dst colormap:(ColormapTypes)colormap NS_SWIFT_NAME(applyColorMap(src:dst:colormap:)); // // void cv::applyColorMap(Mat src, Mat& dst, Mat userColor) // /** * Applies a user colormap on a given image. * * @param src The source image, grayscale or colored of type CV_8UC1 or CV_8UC3. * @param dst The result is the colormapped source image. Note: Mat::create is called on dst. * @param userColor The colormap to apply of type CV_8UC1 or CV_8UC3 and size 256 */ + (void)applyColorMap:(Mat*)src dst:(Mat*)dst userColor:(Mat*)userColor NS_SWIFT_NAME(applyColorMap(src:dst:userColor:)); // // void cv::line(Mat& img, Point pt1, Point pt2, Scalar color, int thickness = 1, LineTypes lineType = LINE_8, int shift = 0) // /** * Draws a line segment connecting two points. * * The function line draws the line segment between pt1 and pt2 points in the image. The line is * clipped by the image boundaries. For non-antialiased lines with integer coordinates, the 8-connected * or 4-connected Bresenham algorithm is used. Thick lines are drawn with rounding endings. Antialiased * lines are drawn using Gaussian filtering. * * @param img Image. * @param pt1 First point of the line segment. * @param pt2 Second point of the line segment. * @param color Line color. * @param thickness Line thickness. * @param lineType Type of the line. See #LineTypes. * @param shift Number of fractional bits in the point coordinates. */ + (void)line:(Mat*)img pt1:(Point2i*)pt1 pt2:(Point2i*)pt2 color:(Scalar*)color thickness:(int)thickness lineType:(LineTypes)lineType shift:(int)shift NS_SWIFT_NAME(line(img:pt1:pt2:color:thickness:lineType:shift:)); /** * Draws a line segment connecting two points. * * The function line draws the line segment between pt1 and pt2 points in the image. The line is * clipped by the image boundaries. For non-antialiased lines with integer coordinates, the 8-connected * or 4-connected Bresenham algorithm is used. Thick lines are drawn with rounding endings. Antialiased * lines are drawn using Gaussian filtering. * * @param img Image. * @param pt1 First point of the line segment. * @param pt2 Second point of the line segment. * @param color Line color. * @param thickness Line thickness. * @param lineType Type of the line. See #LineTypes. */ + (void)line:(Mat*)img pt1:(Point2i*)pt1 pt2:(Point2i*)pt2 color:(Scalar*)color thickness:(int)thickness lineType:(LineTypes)lineType NS_SWIFT_NAME(line(img:pt1:pt2:color:thickness:lineType:)); /** * Draws a line segment connecting two points. * * The function line draws the line segment between pt1 and pt2 points in the image. The line is * clipped by the image boundaries. For non-antialiased lines with integer coordinates, the 8-connected * or 4-connected Bresenham algorithm is used. Thick lines are drawn with rounding endings. Antialiased * lines are drawn using Gaussian filtering. * * @param img Image. * @param pt1 First point of the line segment. * @param pt2 Second point of the line segment. * @param color Line color. * @param thickness Line thickness. */ + (void)line:(Mat*)img pt1:(Point2i*)pt1 pt2:(Point2i*)pt2 color:(Scalar*)color thickness:(int)thickness NS_SWIFT_NAME(line(img:pt1:pt2:color:thickness:)); /** * Draws a line segment connecting two points. * * The function line draws the line segment between pt1 and pt2 points in the image. The line is * clipped by the image boundaries. For non-antialiased lines with integer coordinates, the 8-connected * or 4-connected Bresenham algorithm is used. Thick lines are drawn with rounding endings. Antialiased * lines are drawn using Gaussian filtering. * * @param img Image. * @param pt1 First point of the line segment. * @param pt2 Second point of the line segment. * @param color Line color. */ + (void)line:(Mat*)img pt1:(Point2i*)pt1 pt2:(Point2i*)pt2 color:(Scalar*)color NS_SWIFT_NAME(line(img:pt1:pt2:color:)); // // void cv::arrowedLine(Mat& img, Point pt1, Point pt2, Scalar color, int thickness = 1, LineTypes line_type = 8, int shift = 0, double tipLength = 0.1) // /** * Draws an arrow segment pointing from the first point to the second one. * * The function cv::arrowedLine draws an arrow between pt1 and pt2 points in the image. See also #line. * * @param img Image. * @param pt1 The point the arrow starts from. * @param pt2 The point the arrow points to. * @param color Line color. * @param thickness Line thickness. * @param line_type Type of the line. See #LineTypes * @param shift Number of fractional bits in the point coordinates. * @param tipLength The length of the arrow tip in relation to the arrow length */ + (void)arrowedLine:(Mat*)img pt1:(Point2i*)pt1 pt2:(Point2i*)pt2 color:(Scalar*)color thickness:(int)thickness line_type:(LineTypes)line_type shift:(int)shift tipLength:(double)tipLength NS_SWIFT_NAME(arrowedLine(img:pt1:pt2:color:thickness:line_type:shift:tipLength:)); /** * Draws an arrow segment pointing from the first point to the second one. * * The function cv::arrowedLine draws an arrow between pt1 and pt2 points in the image. See also #line. * * @param img Image. * @param pt1 The point the arrow starts from. * @param pt2 The point the arrow points to. * @param color Line color. * @param thickness Line thickness. * @param line_type Type of the line. See #LineTypes * @param shift Number of fractional bits in the point coordinates. */ + (void)arrowedLine:(Mat*)img pt1:(Point2i*)pt1 pt2:(Point2i*)pt2 color:(Scalar*)color thickness:(int)thickness line_type:(LineTypes)line_type shift:(int)shift NS_SWIFT_NAME(arrowedLine(img:pt1:pt2:color:thickness:line_type:shift:)); /** * Draws an arrow segment pointing from the first point to the second one. * * The function cv::arrowedLine draws an arrow between pt1 and pt2 points in the image. See also #line. * * @param img Image. * @param pt1 The point the arrow starts from. * @param pt2 The point the arrow points to. * @param color Line color. * @param thickness Line thickness. * @param line_type Type of the line. See #LineTypes */ + (void)arrowedLine:(Mat*)img pt1:(Point2i*)pt1 pt2:(Point2i*)pt2 color:(Scalar*)color thickness:(int)thickness line_type:(LineTypes)line_type NS_SWIFT_NAME(arrowedLine(img:pt1:pt2:color:thickness:line_type:)); /** * Draws an arrow segment pointing from the first point to the second one. * * The function cv::arrowedLine draws an arrow between pt1 and pt2 points in the image. See also #line. * * @param img Image. * @param pt1 The point the arrow starts from. * @param pt2 The point the arrow points to. * @param color Line color. * @param thickness Line thickness. */ + (void)arrowedLine:(Mat*)img pt1:(Point2i*)pt1 pt2:(Point2i*)pt2 color:(Scalar*)color thickness:(int)thickness NS_SWIFT_NAME(arrowedLine(img:pt1:pt2:color:thickness:)); /** * Draws an arrow segment pointing from the first point to the second one. * * The function cv::arrowedLine draws an arrow between pt1 and pt2 points in the image. See also #line. * * @param img Image. * @param pt1 The point the arrow starts from. * @param pt2 The point the arrow points to. * @param color Line color. */ + (void)arrowedLine:(Mat*)img pt1:(Point2i*)pt1 pt2:(Point2i*)pt2 color:(Scalar*)color NS_SWIFT_NAME(arrowedLine(img:pt1:pt2:color:)); // // void cv::rectangle(Mat& img, Point pt1, Point pt2, Scalar color, int thickness = 1, LineTypes lineType = LINE_8, int shift = 0) // /** * Draws a simple, thick, or filled up-right rectangle. * * The function cv::rectangle draws a rectangle outline or a filled rectangle whose two opposite corners * are pt1 and pt2. * * @param img Image. * @param pt1 Vertex of the rectangle. * @param pt2 Vertex of the rectangle opposite to pt1 . * @param color Rectangle color or brightness (grayscale image). * @param thickness Thickness of lines that make up the rectangle. Negative values, like #FILLED, * mean that the function has to draw a filled rectangle. * @param lineType Type of the line. See #LineTypes * @param shift Number of fractional bits in the point coordinates. */ + (void)rectangle:(Mat*)img pt1:(Point2i*)pt1 pt2:(Point2i*)pt2 color:(Scalar*)color thickness:(int)thickness lineType:(LineTypes)lineType shift:(int)shift NS_SWIFT_NAME(rectangle(img:pt1:pt2:color:thickness:lineType:shift:)); /** * Draws a simple, thick, or filled up-right rectangle. * * The function cv::rectangle draws a rectangle outline or a filled rectangle whose two opposite corners * are pt1 and pt2. * * @param img Image. * @param pt1 Vertex of the rectangle. * @param pt2 Vertex of the rectangle opposite to pt1 . * @param color Rectangle color or brightness (grayscale image). * @param thickness Thickness of lines that make up the rectangle. Negative values, like #FILLED, * mean that the function has to draw a filled rectangle. * @param lineType Type of the line. See #LineTypes */ + (void)rectangle:(Mat*)img pt1:(Point2i*)pt1 pt2:(Point2i*)pt2 color:(Scalar*)color thickness:(int)thickness lineType:(LineTypes)lineType NS_SWIFT_NAME(rectangle(img:pt1:pt2:color:thickness:lineType:)); /** * Draws a simple, thick, or filled up-right rectangle. * * The function cv::rectangle draws a rectangle outline or a filled rectangle whose two opposite corners * are pt1 and pt2. * * @param img Image. * @param pt1 Vertex of the rectangle. * @param pt2 Vertex of the rectangle opposite to pt1 . * @param color Rectangle color or brightness (grayscale image). * @param thickness Thickness of lines that make up the rectangle. Negative values, like #FILLED, * mean that the function has to draw a filled rectangle. */ + (void)rectangle:(Mat*)img pt1:(Point2i*)pt1 pt2:(Point2i*)pt2 color:(Scalar*)color thickness:(int)thickness NS_SWIFT_NAME(rectangle(img:pt1:pt2:color:thickness:)); /** * Draws a simple, thick, or filled up-right rectangle. * * The function cv::rectangle draws a rectangle outline or a filled rectangle whose two opposite corners * are pt1 and pt2. * * @param img Image. * @param pt1 Vertex of the rectangle. * @param pt2 Vertex of the rectangle opposite to pt1 . * @param color Rectangle color or brightness (grayscale image). * mean that the function has to draw a filled rectangle. */ + (void)rectangle:(Mat*)img pt1:(Point2i*)pt1 pt2:(Point2i*)pt2 color:(Scalar*)color NS_SWIFT_NAME(rectangle(img:pt1:pt2:color:)); // // void cv::rectangle(Mat& img, Rect rec, Scalar color, int thickness = 1, LineTypes lineType = LINE_8, int shift = 0) // /** * * * use `rec` parameter as alternative specification of the drawn rectangle: `r.tl() and * r.br()-Point(1,1)` are opposite corners */ + (void)rectangle:(Mat*)img rec:(Rect2i*)rec color:(Scalar*)color thickness:(int)thickness lineType:(LineTypes)lineType shift:(int)shift NS_SWIFT_NAME(rectangle(img:rec:color:thickness:lineType:shift:)); /** * * * use `rec` parameter as alternative specification of the drawn rectangle: `r.tl() and * r.br()-Point(1,1)` are opposite corners */ + (void)rectangle:(Mat*)img rec:(Rect2i*)rec color:(Scalar*)color thickness:(int)thickness lineType:(LineTypes)lineType NS_SWIFT_NAME(rectangle(img:rec:color:thickness:lineType:)); /** * * * use `rec` parameter as alternative specification of the drawn rectangle: `r.tl() and * r.br()-Point(1,1)` are opposite corners */ + (void)rectangle:(Mat*)img rec:(Rect2i*)rec color:(Scalar*)color thickness:(int)thickness NS_SWIFT_NAME(rectangle(img:rec:color:thickness:)); /** * * * use `rec` parameter as alternative specification of the drawn rectangle: `r.tl() and * r.br()-Point(1,1)` are opposite corners */ + (void)rectangle:(Mat*)img rec:(Rect2i*)rec color:(Scalar*)color NS_SWIFT_NAME(rectangle(img:rec:color:)); // // void cv::circle(Mat& img, Point center, int radius, Scalar color, int thickness = 1, LineTypes lineType = LINE_8, int shift = 0) // /** * Draws a circle. * * The function cv::circle draws a simple or filled circle with a given center and radius. * @param img Image where the circle is drawn. * @param center Center of the circle. * @param radius Radius of the circle. * @param color Circle color. * @param thickness Thickness of the circle outline, if positive. Negative values, like #FILLED, * mean that a filled circle is to be drawn. * @param lineType Type of the circle boundary. See #LineTypes * @param shift Number of fractional bits in the coordinates of the center and in the radius value. */ + (void)circle:(Mat*)img center:(Point2i*)center radius:(int)radius color:(Scalar*)color thickness:(int)thickness lineType:(LineTypes)lineType shift:(int)shift NS_SWIFT_NAME(circle(img:center:radius:color:thickness:lineType:shift:)); /** * Draws a circle. * * The function cv::circle draws a simple or filled circle with a given center and radius. * @param img Image where the circle is drawn. * @param center Center of the circle. * @param radius Radius of the circle. * @param color Circle color. * @param thickness Thickness of the circle outline, if positive. Negative values, like #FILLED, * mean that a filled circle is to be drawn. * @param lineType Type of the circle boundary. See #LineTypes */ + (void)circle:(Mat*)img center:(Point2i*)center radius:(int)radius color:(Scalar*)color thickness:(int)thickness lineType:(LineTypes)lineType NS_SWIFT_NAME(circle(img:center:radius:color:thickness:lineType:)); /** * Draws a circle. * * The function cv::circle draws a simple or filled circle with a given center and radius. * @param img Image where the circle is drawn. * @param center Center of the circle. * @param radius Radius of the circle. * @param color Circle color. * @param thickness Thickness of the circle outline, if positive. Negative values, like #FILLED, * mean that a filled circle is to be drawn. */ + (void)circle:(Mat*)img center:(Point2i*)center radius:(int)radius color:(Scalar*)color thickness:(int)thickness NS_SWIFT_NAME(circle(img:center:radius:color:thickness:)); /** * Draws a circle. * * The function cv::circle draws a simple or filled circle with a given center and radius. * @param img Image where the circle is drawn. * @param center Center of the circle. * @param radius Radius of the circle. * @param color Circle color. * mean that a filled circle is to be drawn. */ + (void)circle:(Mat*)img center:(Point2i*)center radius:(int)radius color:(Scalar*)color NS_SWIFT_NAME(circle(img:center:radius:color:)); // // void cv::ellipse(Mat& img, Point center, Size axes, double angle, double startAngle, double endAngle, Scalar color, int thickness = 1, LineTypes lineType = LINE_8, int shift = 0) // /** * Draws a simple or thick elliptic arc or fills an ellipse sector. * * The function cv::ellipse with more parameters draws an ellipse outline, a filled ellipse, an elliptic * arc, or a filled ellipse sector. The drawing code uses general parametric form. * A piecewise-linear curve is used to approximate the elliptic arc * boundary. If you need more control of the ellipse rendering, you can retrieve the curve using * #ellipse2Poly and then render it with #polylines or fill it with #fillPoly. If you use the first * variant of the function and want to draw the whole ellipse, not an arc, pass `startAngle=0` and * `endAngle=360`. If `startAngle` is greater than `endAngle`, they are swapped. The figure below explains * the meaning of the parameters to draw the blue arc. * * ![Parameters of Elliptic Arc](pics/ellipse.svg) * * @param img Image. * @param center Center of the ellipse. * @param axes Half of the size of the ellipse main axes. * @param angle Ellipse rotation angle in degrees. * @param startAngle Starting angle of the elliptic arc in degrees. * @param endAngle Ending angle of the elliptic arc in degrees. * @param color Ellipse color. * @param thickness Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that * a filled ellipse sector is to be drawn. * @param lineType Type of the ellipse boundary. See #LineTypes * @param shift Number of fractional bits in the coordinates of the center and values of axes. */ + (void)ellipse:(Mat*)img center:(Point2i*)center axes:(Size2i*)axes angle:(double)angle startAngle:(double)startAngle endAngle:(double)endAngle color:(Scalar*)color thickness:(int)thickness lineType:(LineTypes)lineType shift:(int)shift NS_SWIFT_NAME(ellipse(img:center:axes:angle:startAngle:endAngle:color:thickness:lineType:shift:)); /** * Draws a simple or thick elliptic arc or fills an ellipse sector. * * The function cv::ellipse with more parameters draws an ellipse outline, a filled ellipse, an elliptic * arc, or a filled ellipse sector. The drawing code uses general parametric form. * A piecewise-linear curve is used to approximate the elliptic arc * boundary. If you need more control of the ellipse rendering, you can retrieve the curve using * #ellipse2Poly and then render it with #polylines or fill it with #fillPoly. If you use the first * variant of the function and want to draw the whole ellipse, not an arc, pass `startAngle=0` and * `endAngle=360`. If `startAngle` is greater than `endAngle`, they are swapped. The figure below explains * the meaning of the parameters to draw the blue arc. * * ![Parameters of Elliptic Arc](pics/ellipse.svg) * * @param img Image. * @param center Center of the ellipse. * @param axes Half of the size of the ellipse main axes. * @param angle Ellipse rotation angle in degrees. * @param startAngle Starting angle of the elliptic arc in degrees. * @param endAngle Ending angle of the elliptic arc in degrees. * @param color Ellipse color. * @param thickness Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that * a filled ellipse sector is to be drawn. * @param lineType Type of the ellipse boundary. See #LineTypes */ + (void)ellipse:(Mat*)img center:(Point2i*)center axes:(Size2i*)axes angle:(double)angle startAngle:(double)startAngle endAngle:(double)endAngle color:(Scalar*)color thickness:(int)thickness lineType:(LineTypes)lineType NS_SWIFT_NAME(ellipse(img:center:axes:angle:startAngle:endAngle:color:thickness:lineType:)); /** * Draws a simple or thick elliptic arc or fills an ellipse sector. * * The function cv::ellipse with more parameters draws an ellipse outline, a filled ellipse, an elliptic * arc, or a filled ellipse sector. The drawing code uses general parametric form. * A piecewise-linear curve is used to approximate the elliptic arc * boundary. If you need more control of the ellipse rendering, you can retrieve the curve using * #ellipse2Poly and then render it with #polylines or fill it with #fillPoly. If you use the first * variant of the function and want to draw the whole ellipse, not an arc, pass `startAngle=0` and * `endAngle=360`. If `startAngle` is greater than `endAngle`, they are swapped. The figure below explains * the meaning of the parameters to draw the blue arc. * * ![Parameters of Elliptic Arc](pics/ellipse.svg) * * @param img Image. * @param center Center of the ellipse. * @param axes Half of the size of the ellipse main axes. * @param angle Ellipse rotation angle in degrees. * @param startAngle Starting angle of the elliptic arc in degrees. * @param endAngle Ending angle of the elliptic arc in degrees. * @param color Ellipse color. * @param thickness Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that * a filled ellipse sector is to be drawn. */ + (void)ellipse:(Mat*)img center:(Point2i*)center axes:(Size2i*)axes angle:(double)angle startAngle:(double)startAngle endAngle:(double)endAngle color:(Scalar*)color thickness:(int)thickness NS_SWIFT_NAME(ellipse(img:center:axes:angle:startAngle:endAngle:color:thickness:)); /** * Draws a simple or thick elliptic arc or fills an ellipse sector. * * The function cv::ellipse with more parameters draws an ellipse outline, a filled ellipse, an elliptic * arc, or a filled ellipse sector. The drawing code uses general parametric form. * A piecewise-linear curve is used to approximate the elliptic arc * boundary. If you need more control of the ellipse rendering, you can retrieve the curve using * #ellipse2Poly and then render it with #polylines or fill it with #fillPoly. If you use the first * variant of the function and want to draw the whole ellipse, not an arc, pass `startAngle=0` and * `endAngle=360`. If `startAngle` is greater than `endAngle`, they are swapped. The figure below explains * the meaning of the parameters to draw the blue arc. * * ![Parameters of Elliptic Arc](pics/ellipse.svg) * * @param img Image. * @param center Center of the ellipse. * @param axes Half of the size of the ellipse main axes. * @param angle Ellipse rotation angle in degrees. * @param startAngle Starting angle of the elliptic arc in degrees. * @param endAngle Ending angle of the elliptic arc in degrees. * @param color Ellipse color. * a filled ellipse sector is to be drawn. */ + (void)ellipse:(Mat*)img center:(Point2i*)center axes:(Size2i*)axes angle:(double)angle startAngle:(double)startAngle endAngle:(double)endAngle color:(Scalar*)color NS_SWIFT_NAME(ellipse(img:center:axes:angle:startAngle:endAngle:color:)); // // void cv::ellipse(Mat& img, RotatedRect box, Scalar color, int thickness = 1, LineTypes lineType = LINE_8) // /** * * @param img Image. * @param box Alternative ellipse representation via RotatedRect. This means that the function draws * an ellipse inscribed in the rotated rectangle. * @param color Ellipse color. * @param thickness Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that * a filled ellipse sector is to be drawn. * @param lineType Type of the ellipse boundary. See #LineTypes */ + (void)ellipse:(Mat*)img box:(RotatedRect*)box color:(Scalar*)color thickness:(int)thickness lineType:(LineTypes)lineType NS_SWIFT_NAME(ellipse(img:box:color:thickness:lineType:)); /** * * @param img Image. * @param box Alternative ellipse representation via RotatedRect. This means that the function draws * an ellipse inscribed in the rotated rectangle. * @param color Ellipse color. * @param thickness Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that * a filled ellipse sector is to be drawn. */ + (void)ellipse:(Mat*)img box:(RotatedRect*)box color:(Scalar*)color thickness:(int)thickness NS_SWIFT_NAME(ellipse(img:box:color:thickness:)); /** * * @param img Image. * @param box Alternative ellipse representation via RotatedRect. This means that the function draws * an ellipse inscribed in the rotated rectangle. * @param color Ellipse color. * a filled ellipse sector is to be drawn. */ + (void)ellipse:(Mat*)img box:(RotatedRect*)box color:(Scalar*)color NS_SWIFT_NAME(ellipse(img:box:color:)); // // void cv::drawMarker(Mat& img, Point position, Scalar color, MarkerTypes markerType = MARKER_CROSS, int markerSize = 20, int thickness = 1, LineTypes line_type = 8) // /** * Draws a marker on a predefined position in an image. * * The function cv::drawMarker draws a marker on a given position in the image. For the moment several * marker types are supported, see #MarkerTypes for more information. * * @param img Image. * @param position The point where the crosshair is positioned. * @param color Line color. * @param markerType The specific type of marker you want to use, see #MarkerTypes * @param thickness Line thickness. * @param line_type Type of the line, See #LineTypes * @param markerSize The length of the marker axis [default = 20 pixels] */ + (void)drawMarker:(Mat*)img position:(Point2i*)position color:(Scalar*)color markerType:(MarkerTypes)markerType markerSize:(int)markerSize thickness:(int)thickness line_type:(LineTypes)line_type NS_SWIFT_NAME(drawMarker(img:position:color:markerType:markerSize:thickness:line_type:)); /** * Draws a marker on a predefined position in an image. * * The function cv::drawMarker draws a marker on a given position in the image. For the moment several * marker types are supported, see #MarkerTypes for more information. * * @param img Image. * @param position The point where the crosshair is positioned. * @param color Line color. * @param markerType The specific type of marker you want to use, see #MarkerTypes * @param thickness Line thickness. * @param markerSize The length of the marker axis [default = 20 pixels] */ + (void)drawMarker:(Mat*)img position:(Point2i*)position color:(Scalar*)color markerType:(MarkerTypes)markerType markerSize:(int)markerSize thickness:(int)thickness NS_SWIFT_NAME(drawMarker(img:position:color:markerType:markerSize:thickness:)); /** * Draws a marker on a predefined position in an image. * * The function cv::drawMarker draws a marker on a given position in the image. For the moment several * marker types are supported, see #MarkerTypes for more information. * * @param img Image. * @param position The point where the crosshair is positioned. * @param color Line color. * @param markerType The specific type of marker you want to use, see #MarkerTypes * @param markerSize The length of the marker axis [default = 20 pixels] */ + (void)drawMarker:(Mat*)img position:(Point2i*)position color:(Scalar*)color markerType:(MarkerTypes)markerType markerSize:(int)markerSize NS_SWIFT_NAME(drawMarker(img:position:color:markerType:markerSize:)); /** * Draws a marker on a predefined position in an image. * * The function cv::drawMarker draws a marker on a given position in the image. For the moment several * marker types are supported, see #MarkerTypes for more information. * * @param img Image. * @param position The point where the crosshair is positioned. * @param color Line color. * @param markerType The specific type of marker you want to use, see #MarkerTypes */ + (void)drawMarker:(Mat*)img position:(Point2i*)position color:(Scalar*)color markerType:(MarkerTypes)markerType NS_SWIFT_NAME(drawMarker(img:position:color:markerType:)); /** * Draws a marker on a predefined position in an image. * * The function cv::drawMarker draws a marker on a given position in the image. For the moment several * marker types are supported, see #MarkerTypes for more information. * * @param img Image. * @param position The point where the crosshair is positioned. * @param color Line color. */ + (void)drawMarker:(Mat*)img position:(Point2i*)position color:(Scalar*)color NS_SWIFT_NAME(drawMarker(img:position:color:)); // // void cv::fillConvexPoly(Mat& img, vector_Point points, Scalar color, LineTypes lineType = LINE_8, int shift = 0) // /** * Fills a convex polygon. * * The function cv::fillConvexPoly draws a filled convex polygon. This function is much faster than the * function #fillPoly . It can fill not only convex polygons but any monotonic polygon without * self-intersections, that is, a polygon whose contour intersects every horizontal line (scan line) * twice at the most (though, its top-most and/or the bottom edge could be horizontal). * * @param img Image. * @param points Polygon vertices. * @param color Polygon color. * @param lineType Type of the polygon boundaries. See #LineTypes * @param shift Number of fractional bits in the vertex coordinates. */ + (void)fillConvexPoly:(Mat*)img points:(NSArray*)points color:(Scalar*)color lineType:(LineTypes)lineType shift:(int)shift NS_SWIFT_NAME(fillConvexPoly(img:points:color:lineType:shift:)); /** * Fills a convex polygon. * * The function cv::fillConvexPoly draws a filled convex polygon. This function is much faster than the * function #fillPoly . It can fill not only convex polygons but any monotonic polygon without * self-intersections, that is, a polygon whose contour intersects every horizontal line (scan line) * twice at the most (though, its top-most and/or the bottom edge could be horizontal). * * @param img Image. * @param points Polygon vertices. * @param color Polygon color. * @param lineType Type of the polygon boundaries. See #LineTypes */ + (void)fillConvexPoly:(Mat*)img points:(NSArray*)points color:(Scalar*)color lineType:(LineTypes)lineType NS_SWIFT_NAME(fillConvexPoly(img:points:color:lineType:)); /** * Fills a convex polygon. * * The function cv::fillConvexPoly draws a filled convex polygon. This function is much faster than the * function #fillPoly . It can fill not only convex polygons but any monotonic polygon without * self-intersections, that is, a polygon whose contour intersects every horizontal line (scan line) * twice at the most (though, its top-most and/or the bottom edge could be horizontal). * * @param img Image. * @param points Polygon vertices. * @param color Polygon color. */ + (void)fillConvexPoly:(Mat*)img points:(NSArray*)points color:(Scalar*)color NS_SWIFT_NAME(fillConvexPoly(img:points:color:)); // // void cv::fillPoly(Mat& img, vector_vector_Point pts, Scalar color, LineTypes lineType = LINE_8, int shift = 0, Point offset = Point()) // /** * Fills the area bounded by one or more polygons. * * The function cv::fillPoly fills an area bounded by several polygonal contours. The function can fill * complex areas, for example, areas with holes, contours with self-intersections (some of their * parts), and so forth. * * @param img Image. * @param pts Array of polygons where each polygon is represented as an array of points. * @param color Polygon color. * @param lineType Type of the polygon boundaries. See #LineTypes * @param shift Number of fractional bits in the vertex coordinates. * @param offset Optional offset of all points of the contours. */ + (void)fillPoly:(Mat*)img pts:(NSArray*>*)pts color:(Scalar*)color lineType:(LineTypes)lineType shift:(int)shift offset:(Point2i*)offset NS_SWIFT_NAME(fillPoly(img:pts:color:lineType:shift:offset:)); /** * Fills the area bounded by one or more polygons. * * The function cv::fillPoly fills an area bounded by several polygonal contours. The function can fill * complex areas, for example, areas with holes, contours with self-intersections (some of their * parts), and so forth. * * @param img Image. * @param pts Array of polygons where each polygon is represented as an array of points. * @param color Polygon color. * @param lineType Type of the polygon boundaries. See #LineTypes * @param shift Number of fractional bits in the vertex coordinates. */ + (void)fillPoly:(Mat*)img pts:(NSArray*>*)pts color:(Scalar*)color lineType:(LineTypes)lineType shift:(int)shift NS_SWIFT_NAME(fillPoly(img:pts:color:lineType:shift:)); /** * Fills the area bounded by one or more polygons. * * The function cv::fillPoly fills an area bounded by several polygonal contours. The function can fill * complex areas, for example, areas with holes, contours with self-intersections (some of their * parts), and so forth. * * @param img Image. * @param pts Array of polygons where each polygon is represented as an array of points. * @param color Polygon color. * @param lineType Type of the polygon boundaries. See #LineTypes */ + (void)fillPoly:(Mat*)img pts:(NSArray*>*)pts color:(Scalar*)color lineType:(LineTypes)lineType NS_SWIFT_NAME(fillPoly(img:pts:color:lineType:)); /** * Fills the area bounded by one or more polygons. * * The function cv::fillPoly fills an area bounded by several polygonal contours. The function can fill * complex areas, for example, areas with holes, contours with self-intersections (some of their * parts), and so forth. * * @param img Image. * @param pts Array of polygons where each polygon is represented as an array of points. * @param color Polygon color. */ + (void)fillPoly:(Mat*)img pts:(NSArray*>*)pts color:(Scalar*)color NS_SWIFT_NAME(fillPoly(img:pts:color:)); // // void cv::polylines(Mat& img, vector_vector_Point pts, bool isClosed, Scalar color, int thickness = 1, LineTypes lineType = LINE_8, int shift = 0) // /** * Draws several polygonal curves. * * @param img Image. * @param pts Array of polygonal curves. * @param isClosed Flag indicating whether the drawn polylines are closed or not. If they are closed, * the function draws a line from the last vertex of each curve to its first vertex. * @param color Polyline color. * @param thickness Thickness of the polyline edges. * @param lineType Type of the line segments. See #LineTypes * @param shift Number of fractional bits in the vertex coordinates. * * The function cv::polylines draws one or more polygonal curves. */ + (void)polylines:(Mat*)img pts:(NSArray*>*)pts isClosed:(BOOL)isClosed color:(Scalar*)color thickness:(int)thickness lineType:(LineTypes)lineType shift:(int)shift NS_SWIFT_NAME(polylines(img:pts:isClosed:color:thickness:lineType:shift:)); /** * Draws several polygonal curves. * * @param img Image. * @param pts Array of polygonal curves. * @param isClosed Flag indicating whether the drawn polylines are closed or not. If they are closed, * the function draws a line from the last vertex of each curve to its first vertex. * @param color Polyline color. * @param thickness Thickness of the polyline edges. * @param lineType Type of the line segments. See #LineTypes * * The function cv::polylines draws one or more polygonal curves. */ + (void)polylines:(Mat*)img pts:(NSArray*>*)pts isClosed:(BOOL)isClosed color:(Scalar*)color thickness:(int)thickness lineType:(LineTypes)lineType NS_SWIFT_NAME(polylines(img:pts:isClosed:color:thickness:lineType:)); /** * Draws several polygonal curves. * * @param img Image. * @param pts Array of polygonal curves. * @param isClosed Flag indicating whether the drawn polylines are closed or not. If they are closed, * the function draws a line from the last vertex of each curve to its first vertex. * @param color Polyline color. * @param thickness Thickness of the polyline edges. * * The function cv::polylines draws one or more polygonal curves. */ + (void)polylines:(Mat*)img pts:(NSArray*>*)pts isClosed:(BOOL)isClosed color:(Scalar*)color thickness:(int)thickness NS_SWIFT_NAME(polylines(img:pts:isClosed:color:thickness:)); /** * Draws several polygonal curves. * * @param img Image. * @param pts Array of polygonal curves. * @param isClosed Flag indicating whether the drawn polylines are closed or not. If they are closed, * the function draws a line from the last vertex of each curve to its first vertex. * @param color Polyline color. * * The function cv::polylines draws one or more polygonal curves. */ + (void)polylines:(Mat*)img pts:(NSArray*>*)pts isClosed:(BOOL)isClosed color:(Scalar*)color NS_SWIFT_NAME(polylines(img:pts:isClosed:color:)); // // void cv::drawContours(Mat& image, vector_vector_Point contours, int contourIdx, Scalar color, int thickness = 1, LineTypes lineType = LINE_8, Mat hierarchy = Mat(), int maxLevel = INT_MAX, Point offset = Point()) // /** * Draws contours outlines or filled contours. * * The function draws contour outlines in the image if `$$\texttt{thickness} \ge 0$$` or fills the area * bounded by the contours if `$$\texttt{thickness}<0$$` . The example below shows how to retrieve * connected components from the binary image and label them: : * INCLUDE: snippets/imgproc_drawContours.cpp * * @param image Destination image. * @param contours All the input contours. Each contour is stored as a point vector. * @param contourIdx Parameter indicating a contour to draw. If it is negative, all the contours are drawn. * @param color Color of the contours. * @param thickness Thickness of lines the contours are drawn with. If it is negative (for example, * thickness=#FILLED ), the contour interiors are drawn. * @param lineType Line connectivity. See #LineTypes * @param hierarchy Optional information about hierarchy. It is only needed if you want to draw only * some of the contours (see maxLevel ). * @param maxLevel Maximal level for drawn contours. If it is 0, only the specified contour is drawn. * If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function * draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This * parameter is only taken into account when there is hierarchy available. * @param offset Optional contour shift parameter. Shift all the drawn contours by the specified * `$$\texttt{offset}=(dx,dy)$$` . * NOTE: When thickness=#FILLED, the function is designed to handle connected components with holes correctly * even when no hierarchy data is provided. This is done by analyzing all the outlines together * using even-odd rule. This may give incorrect results if you have a joint collection of separately retrieved * contours. In order to solve this problem, you need to call #drawContours separately for each sub-group * of contours, or iterate over the collection using contourIdx parameter. */ + (void)drawContours:(Mat*)image contours:(NSArray*>*)contours contourIdx:(int)contourIdx color:(Scalar*)color thickness:(int)thickness lineType:(LineTypes)lineType hierarchy:(Mat*)hierarchy maxLevel:(int)maxLevel offset:(Point2i*)offset NS_SWIFT_NAME(drawContours(image:contours:contourIdx:color:thickness:lineType:hierarchy:maxLevel:offset:)); /** * Draws contours outlines or filled contours. * * The function draws contour outlines in the image if `$$\texttt{thickness} \ge 0$$` or fills the area * bounded by the contours if `$$\texttt{thickness}<0$$` . The example below shows how to retrieve * connected components from the binary image and label them: : * INCLUDE: snippets/imgproc_drawContours.cpp * * @param image Destination image. * @param contours All the input contours. Each contour is stored as a point vector. * @param contourIdx Parameter indicating a contour to draw. If it is negative, all the contours are drawn. * @param color Color of the contours. * @param thickness Thickness of lines the contours are drawn with. If it is negative (for example, * thickness=#FILLED ), the contour interiors are drawn. * @param lineType Line connectivity. See #LineTypes * @param hierarchy Optional information about hierarchy. It is only needed if you want to draw only * some of the contours (see maxLevel ). * @param maxLevel Maximal level for drawn contours. If it is 0, only the specified contour is drawn. * If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function * draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This * parameter is only taken into account when there is hierarchy available. * `$$\texttt{offset}=(dx,dy)$$` . * NOTE: When thickness=#FILLED, the function is designed to handle connected components with holes correctly * even when no hierarchy data is provided. This is done by analyzing all the outlines together * using even-odd rule. This may give incorrect results if you have a joint collection of separately retrieved * contours. In order to solve this problem, you need to call #drawContours separately for each sub-group * of contours, or iterate over the collection using contourIdx parameter. */ + (void)drawContours:(Mat*)image contours:(NSArray*>*)contours contourIdx:(int)contourIdx color:(Scalar*)color thickness:(int)thickness lineType:(LineTypes)lineType hierarchy:(Mat*)hierarchy maxLevel:(int)maxLevel NS_SWIFT_NAME(drawContours(image:contours:contourIdx:color:thickness:lineType:hierarchy:maxLevel:)); /** * Draws contours outlines or filled contours. * * The function draws contour outlines in the image if `$$\texttt{thickness} \ge 0$$` or fills the area * bounded by the contours if `$$\texttt{thickness}<0$$` . The example below shows how to retrieve * connected components from the binary image and label them: : * INCLUDE: snippets/imgproc_drawContours.cpp * * @param image Destination image. * @param contours All the input contours. Each contour is stored as a point vector. * @param contourIdx Parameter indicating a contour to draw. If it is negative, all the contours are drawn. * @param color Color of the contours. * @param thickness Thickness of lines the contours are drawn with. If it is negative (for example, * thickness=#FILLED ), the contour interiors are drawn. * @param lineType Line connectivity. See #LineTypes * @param hierarchy Optional information about hierarchy. It is only needed if you want to draw only * some of the contours (see maxLevel ). * If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function * draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This * parameter is only taken into account when there is hierarchy available. * `$$\texttt{offset}=(dx,dy)$$` . * NOTE: When thickness=#FILLED, the function is designed to handle connected components with holes correctly * even when no hierarchy data is provided. This is done by analyzing all the outlines together * using even-odd rule. This may give incorrect results if you have a joint collection of separately retrieved * contours. In order to solve this problem, you need to call #drawContours separately for each sub-group * of contours, or iterate over the collection using contourIdx parameter. */ + (void)drawContours:(Mat*)image contours:(NSArray*>*)contours contourIdx:(int)contourIdx color:(Scalar*)color thickness:(int)thickness lineType:(LineTypes)lineType hierarchy:(Mat*)hierarchy NS_SWIFT_NAME(drawContours(image:contours:contourIdx:color:thickness:lineType:hierarchy:)); /** * Draws contours outlines or filled contours. * * The function draws contour outlines in the image if `$$\texttt{thickness} \ge 0$$` or fills the area * bounded by the contours if `$$\texttt{thickness}<0$$` . The example below shows how to retrieve * connected components from the binary image and label them: : * INCLUDE: snippets/imgproc_drawContours.cpp * * @param image Destination image. * @param contours All the input contours. Each contour is stored as a point vector. * @param contourIdx Parameter indicating a contour to draw. If it is negative, all the contours are drawn. * @param color Color of the contours. * @param thickness Thickness of lines the contours are drawn with. If it is negative (for example, * thickness=#FILLED ), the contour interiors are drawn. * @param lineType Line connectivity. See #LineTypes * some of the contours (see maxLevel ). * If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function * draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This * parameter is only taken into account when there is hierarchy available. * `$$\texttt{offset}=(dx,dy)$$` . * NOTE: When thickness=#FILLED, the function is designed to handle connected components with holes correctly * even when no hierarchy data is provided. This is done by analyzing all the outlines together * using even-odd rule. This may give incorrect results if you have a joint collection of separately retrieved * contours. In order to solve this problem, you need to call #drawContours separately for each sub-group * of contours, or iterate over the collection using contourIdx parameter. */ + (void)drawContours:(Mat*)image contours:(NSArray*>*)contours contourIdx:(int)contourIdx color:(Scalar*)color thickness:(int)thickness lineType:(LineTypes)lineType NS_SWIFT_NAME(drawContours(image:contours:contourIdx:color:thickness:lineType:)); /** * Draws contours outlines or filled contours. * * The function draws contour outlines in the image if `$$\texttt{thickness} \ge 0$$` or fills the area * bounded by the contours if `$$\texttt{thickness}<0$$` . The example below shows how to retrieve * connected components from the binary image and label them: : * INCLUDE: snippets/imgproc_drawContours.cpp * * @param image Destination image. * @param contours All the input contours. Each contour is stored as a point vector. * @param contourIdx Parameter indicating a contour to draw. If it is negative, all the contours are drawn. * @param color Color of the contours. * @param thickness Thickness of lines the contours are drawn with. If it is negative (for example, * thickness=#FILLED ), the contour interiors are drawn. * some of the contours (see maxLevel ). * If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function * draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This * parameter is only taken into account when there is hierarchy available. * `$$\texttt{offset}=(dx,dy)$$` . * NOTE: When thickness=#FILLED, the function is designed to handle connected components with holes correctly * even when no hierarchy data is provided. This is done by analyzing all the outlines together * using even-odd rule. This may give incorrect results if you have a joint collection of separately retrieved * contours. In order to solve this problem, you need to call #drawContours separately for each sub-group * of contours, or iterate over the collection using contourIdx parameter. */ + (void)drawContours:(Mat*)image contours:(NSArray*>*)contours contourIdx:(int)contourIdx color:(Scalar*)color thickness:(int)thickness NS_SWIFT_NAME(drawContours(image:contours:contourIdx:color:thickness:)); /** * Draws contours outlines or filled contours. * * The function draws contour outlines in the image if `$$\texttt{thickness} \ge 0$$` or fills the area * bounded by the contours if `$$\texttt{thickness}<0$$` . The example below shows how to retrieve * connected components from the binary image and label them: : * INCLUDE: snippets/imgproc_drawContours.cpp * * @param image Destination image. * @param contours All the input contours. Each contour is stored as a point vector. * @param contourIdx Parameter indicating a contour to draw. If it is negative, all the contours are drawn. * @param color Color of the contours. * thickness=#FILLED ), the contour interiors are drawn. * some of the contours (see maxLevel ). * If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function * draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This * parameter is only taken into account when there is hierarchy available. * `$$\texttt{offset}=(dx,dy)$$` . * NOTE: When thickness=#FILLED, the function is designed to handle connected components with holes correctly * even when no hierarchy data is provided. This is done by analyzing all the outlines together * using even-odd rule. This may give incorrect results if you have a joint collection of separately retrieved * contours. In order to solve this problem, you need to call #drawContours separately for each sub-group * of contours, or iterate over the collection using contourIdx parameter. */ + (void)drawContours:(Mat*)image contours:(NSArray*>*)contours contourIdx:(int)contourIdx color:(Scalar*)color NS_SWIFT_NAME(drawContours(image:contours:contourIdx:color:)); // // bool cv::clipLine(Rect imgRect, Point& pt1, Point& pt2) // /** * * @param imgRect Image rectangle. * @param pt1 First line point. * @param pt2 Second line point. */ + (BOOL)clipLine:(Rect2i*)imgRect pt1:(Point2i*)pt1 pt2:(Point2i*)pt2 NS_SWIFT_NAME(clipLine(imgRect:pt1:pt2:)); // // void cv::ellipse2Poly(Point center, Size axes, int angle, int arcStart, int arcEnd, int delta, vector_Point& pts) // /** * Approximates an elliptic arc with a polyline. * * The function ellipse2Poly computes the vertices of a polyline that approximates the specified * elliptic arc. It is used by #ellipse. If `arcStart` is greater than `arcEnd`, they are swapped. * * @param center Center of the arc. * @param axes Half of the size of the ellipse main axes. See #ellipse for details. * @param angle Rotation angle of the ellipse in degrees. See #ellipse for details. * @param arcStart Starting angle of the elliptic arc in degrees. * @param arcEnd Ending angle of the elliptic arc in degrees. * @param delta Angle between the subsequent polyline vertices. It defines the approximation * accuracy. * @param pts Output vector of polyline vertices. */ + (void)ellipse2Poly:(Point2i*)center axes:(Size2i*)axes angle:(int)angle arcStart:(int)arcStart arcEnd:(int)arcEnd delta:(int)delta pts:(NSMutableArray*)pts NS_SWIFT_NAME(ellipse2Poly(center:axes:angle:arcStart:arcEnd:delta:pts:)); // // void cv::putText(Mat& img, String text, Point org, HersheyFonts fontFace, double fontScale, Scalar color, int thickness = 1, LineTypes lineType = LINE_8, bool bottomLeftOrigin = false) // /** * Draws a text string. * * The function cv::putText renders the specified text string in the image. Symbols that cannot be rendered * using the specified font are replaced by question marks. See #getTextSize for a text rendering code * example. * * @param img Image. * @param text Text string to be drawn. * @param org Bottom-left corner of the text string in the image. * @param fontFace Font type, see #HersheyFonts. * @param fontScale Font scale factor that is multiplied by the font-specific base size. * @param color Text color. * @param thickness Thickness of the lines used to draw a text. * @param lineType Line type. See #LineTypes * @param bottomLeftOrigin When true, the image data origin is at the bottom-left corner. Otherwise, * it is at the top-left corner. */ + (void)putText:(Mat*)img text:(NSString*)text org:(Point2i*)org fontFace:(HersheyFonts)fontFace fontScale:(double)fontScale color:(Scalar*)color thickness:(int)thickness lineType:(LineTypes)lineType bottomLeftOrigin:(BOOL)bottomLeftOrigin NS_SWIFT_NAME(putText(img:text:org:fontFace:fontScale:color:thickness:lineType:bottomLeftOrigin:)); /** * Draws a text string. * * The function cv::putText renders the specified text string in the image. Symbols that cannot be rendered * using the specified font are replaced by question marks. See #getTextSize for a text rendering code * example. * * @param img Image. * @param text Text string to be drawn. * @param org Bottom-left corner of the text string in the image. * @param fontFace Font type, see #HersheyFonts. * @param fontScale Font scale factor that is multiplied by the font-specific base size. * @param color Text color. * @param thickness Thickness of the lines used to draw a text. * @param lineType Line type. See #LineTypes * it is at the top-left corner. */ + (void)putText:(Mat*)img text:(NSString*)text org:(Point2i*)org fontFace:(HersheyFonts)fontFace fontScale:(double)fontScale color:(Scalar*)color thickness:(int)thickness lineType:(LineTypes)lineType NS_SWIFT_NAME(putText(img:text:org:fontFace:fontScale:color:thickness:lineType:)); /** * Draws a text string. * * The function cv::putText renders the specified text string in the image. Symbols that cannot be rendered * using the specified font are replaced by question marks. See #getTextSize for a text rendering code * example. * * @param img Image. * @param text Text string to be drawn. * @param org Bottom-left corner of the text string in the image. * @param fontFace Font type, see #HersheyFonts. * @param fontScale Font scale factor that is multiplied by the font-specific base size. * @param color Text color. * @param thickness Thickness of the lines used to draw a text. * it is at the top-left corner. */ + (void)putText:(Mat*)img text:(NSString*)text org:(Point2i*)org fontFace:(HersheyFonts)fontFace fontScale:(double)fontScale color:(Scalar*)color thickness:(int)thickness NS_SWIFT_NAME(putText(img:text:org:fontFace:fontScale:color:thickness:)); /** * Draws a text string. * * The function cv::putText renders the specified text string in the image. Symbols that cannot be rendered * using the specified font are replaced by question marks. See #getTextSize for a text rendering code * example. * * @param img Image. * @param text Text string to be drawn. * @param org Bottom-left corner of the text string in the image. * @param fontFace Font type, see #HersheyFonts. * @param fontScale Font scale factor that is multiplied by the font-specific base size. * @param color Text color. * it is at the top-left corner. */ + (void)putText:(Mat*)img text:(NSString*)text org:(Point2i*)org fontFace:(HersheyFonts)fontFace fontScale:(double)fontScale color:(Scalar*)color NS_SWIFT_NAME(putText(img:text:org:fontFace:fontScale:color:)); // // Size cv::getTextSize(String text, HersheyFonts fontFace, double fontScale, int thickness, int* baseLine) // /** * Calculates the width and height of a text string. * * The function cv::getTextSize calculates and returns the size of a box that contains the specified text. * That is, the following code renders some text, the tight box surrounding it, and the baseline: : * * String text = "Funny text inside the box"; * int fontFace = FONT_HERSHEY_SCRIPT_SIMPLEX; * double fontScale = 2; * int thickness = 3; * * Mat img(600, 800, CV_8UC3, Scalar::all(0)); * * int baseline=0; * Size textSize = getTextSize(text, fontFace, * fontScale, thickness, &baseline); * baseline += thickness; * * // center the text * Point textOrg((img.cols - textSize.width)/2, * (img.rows + textSize.height)/2); * * // draw the box * rectangle(img, textOrg + Point(0, baseline), * textOrg + Point(textSize.width, -textSize.height), * Scalar(0,0,255)); * // ... and the baseline first * line(img, textOrg + Point(0, thickness), * textOrg + Point(textSize.width, thickness), * Scalar(0, 0, 255)); * * // then put the text itself * putText(img, text, textOrg, fontFace, fontScale, * Scalar::all(255), thickness, 8); * * * @param text Input text string. * @param fontFace Font to use, see #HersheyFonts. * @param fontScale Font scale factor that is multiplied by the font-specific base size. * @param thickness Thickness of lines used to render the text. See #putText for details. * @param baseLine y-coordinate of the baseline relative to the bottom-most text * point. * @return The size of a box that contains the specified text. * * @see `+putText:text:org:fontFace:fontScale:color:thickness:lineType:bottomLeftOrigin:` */ + (Size2i*)getTextSize:(NSString*)text fontFace:(HersheyFonts)fontFace fontScale:(double)fontScale thickness:(int)thickness baseLine:(int*)baseLine NS_SWIFT_NAME(getTextSize(text:fontFace:fontScale:thickness:baseLine:)); // // double cv::getFontScaleFromHeight(int fontFace, int pixelHeight, int thickness = 1) // /** * Calculates the font-specific size to use to achieve a given height in pixels. * * @param fontFace Font to use, see cv::HersheyFonts. * @param pixelHeight Pixel height to compute the fontScale for * @param thickness Thickness of lines used to render the text.See putText for details. * @return The fontSize to use for cv::putText * * @see `cv::putText` */ + (double)getFontScaleFromHeight:(int)fontFace pixelHeight:(int)pixelHeight thickness:(int)thickness NS_SWIFT_NAME(getFontScaleFromHeight(fontFace:pixelHeight:thickness:)); /** * Calculates the font-specific size to use to achieve a given height in pixels. * * @param fontFace Font to use, see cv::HersheyFonts. * @param pixelHeight Pixel height to compute the fontScale for * @return The fontSize to use for cv::putText * * @see `cv::putText` */ + (double)getFontScaleFromHeight:(int)fontFace pixelHeight:(int)pixelHeight NS_SWIFT_NAME(getFontScaleFromHeight(fontFace:pixelHeight:)); // // void cv::HoughLinesWithAccumulator(Mat image, Mat& lines, double rho, double theta, int threshold, double srn = 0, double stn = 0, double min_theta = 0, double max_theta = CV_PI) // /** * Finds lines in a binary image using the standard Hough transform and get accumulator. * * NOTE: This function is for bindings use only. Use original function in C++ code * * @see `+HoughLines:lines:rho:theta:threshold:srn:stn:min_theta:max_theta:` */ + (void)HoughLinesWithAccumulator:(Mat*)image lines:(Mat*)lines rho:(double)rho theta:(double)theta threshold:(int)threshold srn:(double)srn stn:(double)stn min_theta:(double)min_theta max_theta:(double)max_theta NS_SWIFT_NAME(HoughLinesWithAccumulator(image:lines:rho:theta:threshold:srn:stn:min_theta:max_theta:)); /** * Finds lines in a binary image using the standard Hough transform and get accumulator. * * NOTE: This function is for bindings use only. Use original function in C++ code * * @see `+HoughLines:lines:rho:theta:threshold:srn:stn:min_theta:max_theta:` */ + (void)HoughLinesWithAccumulator:(Mat*)image lines:(Mat*)lines rho:(double)rho theta:(double)theta threshold:(int)threshold srn:(double)srn stn:(double)stn min_theta:(double)min_theta NS_SWIFT_NAME(HoughLinesWithAccumulator(image:lines:rho:theta:threshold:srn:stn:min_theta:)); /** * Finds lines in a binary image using the standard Hough transform and get accumulator. * * NOTE: This function is for bindings use only. Use original function in C++ code * * @see `+HoughLines:lines:rho:theta:threshold:srn:stn:min_theta:max_theta:` */ + (void)HoughLinesWithAccumulator:(Mat*)image lines:(Mat*)lines rho:(double)rho theta:(double)theta threshold:(int)threshold srn:(double)srn stn:(double)stn NS_SWIFT_NAME(HoughLinesWithAccumulator(image:lines:rho:theta:threshold:srn:stn:)); /** * Finds lines in a binary image using the standard Hough transform and get accumulator. * * NOTE: This function is for bindings use only. Use original function in C++ code * * @see `+HoughLines:lines:rho:theta:threshold:srn:stn:min_theta:max_theta:` */ + (void)HoughLinesWithAccumulator:(Mat*)image lines:(Mat*)lines rho:(double)rho theta:(double)theta threshold:(int)threshold srn:(double)srn NS_SWIFT_NAME(HoughLinesWithAccumulator(image:lines:rho:theta:threshold:srn:)); /** * Finds lines in a binary image using the standard Hough transform and get accumulator. * * NOTE: This function is for bindings use only. Use original function in C++ code * * @see `+HoughLines:lines:rho:theta:threshold:srn:stn:min_theta:max_theta:` */ + (void)HoughLinesWithAccumulator:(Mat*)image lines:(Mat*)lines rho:(double)rho theta:(double)theta threshold:(int)threshold NS_SWIFT_NAME(HoughLinesWithAccumulator(image:lines:rho:theta:threshold:)); @end NS_ASSUME_NONNULL_END